Mobile Price Project Using Classification Algorithms ( Decission Trees, Random Forest, SVM )¶

pic.jpeg

Import libraries¶

In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler


plt.style.use('ggplot')
In [5]:
# show all columns
pd.set_option('display.max_columns', 50)

Import Datasets¶

In [6]:
train_data = pd.read_csv('phone_train.csv')
test_data = pd.read_csv('phone_test.csv')
In [7]:
df_train = pd.DataFrame(train_data)
df_test = pd.DataFrame(test_data)
In [8]:
df_train
Out[8]:
battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi price_range
0 842 0 2.2 0 1 0 7 0.6 188 2 2 20 756 2549 9 7 19 0 0 1 1
1 1021 1 0.5 1 0 1 53 0.7 136 3 6 905 1988 2631 17 3 7 1 1 0 2
2 563 1 0.5 1 2 1 41 0.9 145 5 6 1263 1716 2603 11 2 9 1 1 0 2
3 615 1 2.5 0 0 0 10 0.8 131 6 9 1216 1786 2769 16 8 11 1 0 0 2
4 1821 1 1.2 0 13 1 44 0.6 141 2 14 1208 1212 1411 8 2 15 1 1 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1995 794 1 0.5 1 0 1 2 0.8 106 6 14 1222 1890 668 13 4 19 1 1 0 0
1996 1965 1 2.6 1 0 0 39 0.2 187 4 3 915 1965 2032 11 10 16 1 1 1 2
1997 1911 0 0.9 1 1 1 36 0.7 108 8 3 868 1632 3057 9 1 5 1 1 0 3
1998 1512 0 0.9 0 4 1 46 0.1 145 5 5 336 670 869 18 10 19 1 1 1 0
1999 510 1 2.0 1 5 1 45 0.9 168 6 16 483 754 3919 19 4 2 1 1 1 3

2000 rows × 21 columns

In [9]:
df_test
Out[9]:
id battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi
0 1 1043 1 1.8 1 14 0 5 0.1 193 3 16 226 1412 3476 12 7 2 0 1 0
1 2 841 1 0.5 1 4 1 61 0.8 191 5 12 746 857 3895 6 0 7 1 0 0
2 3 1807 1 2.8 0 1 0 27 0.9 186 3 4 1270 1366 2396 17 10 10 0 1 1
3 4 1546 0 0.5 1 18 1 25 0.5 96 8 20 295 1752 3893 10 0 7 1 1 0
4 5 1434 0 1.4 0 11 1 49 0.5 108 6 18 749 810 1773 15 8 7 1 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
995 996 1700 1 1.9 0 0 1 54 0.5 170 7 17 644 913 2121 14 8 15 1 1 0
996 997 609 0 1.8 1 0 0 13 0.9 186 4 2 1152 1632 1933 8 1 19 0 1 1
997 998 1185 0 1.4 0 1 1 8 0.5 80 1 12 477 825 1223 5 0 14 1 0 0
998 999 1533 1 0.5 1 0 0 50 0.4 171 2 12 38 832 2509 15 11 6 0 1 0
999 1000 1270 1 0.5 0 4 1 35 0.1 140 6 19 457 608 2828 9 2 3 1 0 1

1000 rows × 21 columns

Preprocesing¶

Checking the similarity of the test data with the train data
In [10]:
df_train.describe()
Out[10]:
battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi price_range
count 2000.000000 2000.0000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000
mean 1238.518500 0.4950 1.522250 0.509500 4.309500 0.521500 32.046500 0.501750 140.249000 4.520500 9.916500 645.108000 1251.515500 2124.213000 12.306500 5.767000 11.011000 0.761500 0.503000 0.507000 1.500000
std 439.418206 0.5001 0.816004 0.500035 4.341444 0.499662 18.145715 0.288416 35.399655 2.287837 6.064315 443.780811 432.199447 1084.732044 4.213245 4.356398 5.463955 0.426273 0.500116 0.500076 1.118314
min 501.000000 0.0000 0.500000 0.000000 0.000000 0.000000 2.000000 0.100000 80.000000 1.000000 0.000000 0.000000 500.000000 256.000000 5.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000
25% 851.750000 0.0000 0.700000 0.000000 1.000000 0.000000 16.000000 0.200000 109.000000 3.000000 5.000000 282.750000 874.750000 1207.500000 9.000000 2.000000 6.000000 1.000000 0.000000 0.000000 0.750000
50% 1226.000000 0.0000 1.500000 1.000000 3.000000 1.000000 32.000000 0.500000 141.000000 4.000000 10.000000 564.000000 1247.000000 2146.500000 12.000000 5.000000 11.000000 1.000000 1.000000 1.000000 1.500000
75% 1615.250000 1.0000 2.200000 1.000000 7.000000 1.000000 48.000000 0.800000 170.000000 7.000000 15.000000 947.250000 1633.000000 3064.500000 16.000000 9.000000 16.000000 1.000000 1.000000 1.000000 2.250000
max 1998.000000 1.0000 3.000000 1.000000 19.000000 1.000000 64.000000 1.000000 200.000000 8.000000 20.000000 1960.000000 1998.000000 3998.000000 19.000000 18.000000 20.000000 1.000000 1.000000 1.000000 3.000000
In [11]:
df_test.describe()
Out[11]:
id battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi
count 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.00000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.00000 1000.000000
mean 500.500000 1248.510000 0.516000 1.540900 0.517000 4.593000 0.487000 33.652000 0.517500 139.51100 4.328000 10.054000 627.121000 1239.774000 2138.998000 11.995000 5.316000 11.085000 0.756000 0.50000 0.507000
std 288.819436 432.458227 0.499994 0.829268 0.499961 4.463325 0.500081 18.128694 0.280861 34.85155 2.288155 6.095099 432.929699 439.670981 1088.092278 4.320607 4.240062 5.497636 0.429708 0.50025 0.500201
min 1.000000 500.000000 0.000000 0.500000 0.000000 0.000000 0.000000 2.000000 0.100000 80.00000 1.000000 0.000000 0.000000 501.000000 263.000000 5.000000 0.000000 2.000000 0.000000 0.00000 0.000000
25% 250.750000 895.000000 0.000000 0.700000 0.000000 1.000000 0.000000 18.000000 0.300000 109.75000 2.000000 5.000000 263.750000 831.750000 1237.250000 8.000000 2.000000 6.750000 1.000000 0.00000 0.000000
50% 500.500000 1246.500000 1.000000 1.500000 1.000000 3.000000 0.000000 34.500000 0.500000 139.00000 4.000000 10.000000 564.500000 1250.000000 2153.500000 12.000000 5.000000 11.000000 1.000000 0.50000 1.000000
75% 750.250000 1629.250000 1.000000 2.300000 1.000000 7.000000 1.000000 49.000000 0.800000 170.00000 6.000000 16.000000 903.000000 1637.750000 3065.500000 16.000000 8.000000 16.000000 1.000000 1.00000 1.000000
max 1000.000000 1999.000000 1.000000 3.000000 1.000000 19.000000 1.000000 64.000000 1.000000 200.00000 8.000000 20.000000 1907.000000 1998.000000 3989.000000 19.000000 18.000000 20.000000 1.000000 1.00000 1.000000
They're similar
Missing values of train dataset
In [12]:
m = df_train.isnull().sum()
if m.all() == 0:
    print('Missing values *train dataset*  =>>> False')
else:
    print('We have missing values in train dataset:\n', m)
Missing values *train dataset*  =>>> False
Missing values of test dataset
In [13]:
m = df_test.isnull().sum()
if m.all() == 0:
    print('Missing values *test dataset* =>>> False')
else:
    print('We have missing values in test dataset:\n', m)
Missing values *test dataset* =>>> False
Visualization of train dataset
In [14]:
df_train.hist(bins=100,figsize=(30,20))
plt.show()
No description has been provided for this image
In [15]:
plt.figure(figsize=(15, 10))
sns.heatmap(df_train.corr(numeric_only=[True]), cmap='Reds', cbar=True, annot=True, linewidths=0.5, annot_kws={"size": 8})
plt.title('Correlation Heatmap')
plt.xticks()
plt.xlabel('Features')
plt.ylabel('Features')
plt.show()
No description has been provided for this image
In [16]:
plt.figure(figsize=(15,5)) 
plt.scatter(x=df_train['ram'], y=df_train['price_range'])
plt.title('compare ram vs target',
            backgroundcolor='black',color='red',fontsize=15)
plt.xlabel('ram', fontsize = 15)
plt.ylabel('Price range', fontsize=15)
Out[16]:
Text(0, 0.5, 'Price range')
No description has been provided for this image
In [17]:
fig = px.scatter(df_train, x='ram', y='price_range',
                  color='price_range', marginal_y='violin', marginal_x='box')

fig.update_layout(
    title='Compare RAM vs Price Range',
    title_x=0.5
)

fig.show()
In [18]:
sns.jointplot(x='ram', y='price_range', data=df_train, color='red', kind='kde')
Out[18]:
<seaborn.axisgrid.JointGrid at 0x1f2f0997590>
No description has been provided for this image
In [19]:
fig = px.density_contour(df_train, x='ram', y='price_range')

fig.update_layout(
    title='Compare RAM vs Price Range',
    title_x=0.5,
    title_yanchor='top'
)

fig.show()
In [20]:
labels = ['3G-Supported', 'Not Supported']
values = df_train['three_g'].value_counts().values

fig = px.pie(values=values, 
              names=labels,
              hole=.3
            )
fig.update_layout(
    title_text="3G",
    title_x=0.45, 
    title_yanchor="middle"
)

fig.show()
In [21]:
labels = ['4G-Supported', 'Not Supported']
values = df_train['four_g'].value_counts().values

fig = px.pie(values=values,  
             names=labels,
             hole=.3
            )

fig.update_layout(
    title_text="4G",
    title_x=0.45, 
    title_yanchor="middle"
)

fig.show()
In [22]:
labels = ['1', '2', '3', '4', '5', '6', '7', '8']
values = df_train['n_cores'].value_counts().values

fig = px.pie(values=values,  
             names=labels,
             hole=0.3
            )

fig.update_layout(
    title_text="n_cores",
    title_x=0.5, 
    title_yanchor="middle"
)

fig.show()
In [23]:
df_train.describe()
Out[23]:
battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi price_range
count 2000.000000 2000.0000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000 2000.000000
mean 1238.518500 0.4950 1.522250 0.509500 4.309500 0.521500 32.046500 0.501750 140.249000 4.520500 9.916500 645.108000 1251.515500 2124.213000 12.306500 5.767000 11.011000 0.761500 0.503000 0.507000 1.500000
std 439.418206 0.5001 0.816004 0.500035 4.341444 0.499662 18.145715 0.288416 35.399655 2.287837 6.064315 443.780811 432.199447 1084.732044 4.213245 4.356398 5.463955 0.426273 0.500116 0.500076 1.118314
min 501.000000 0.0000 0.500000 0.000000 0.000000 0.000000 2.000000 0.100000 80.000000 1.000000 0.000000 0.000000 500.000000 256.000000 5.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000
25% 851.750000 0.0000 0.700000 0.000000 1.000000 0.000000 16.000000 0.200000 109.000000 3.000000 5.000000 282.750000 874.750000 1207.500000 9.000000 2.000000 6.000000 1.000000 0.000000 0.000000 0.750000
50% 1226.000000 0.0000 1.500000 1.000000 3.000000 1.000000 32.000000 0.500000 141.000000 4.000000 10.000000 564.000000 1247.000000 2146.500000 12.000000 5.000000 11.000000 1.000000 1.000000 1.000000 1.500000
75% 1615.250000 1.0000 2.200000 1.000000 7.000000 1.000000 48.000000 0.800000 170.000000 7.000000 15.000000 947.250000 1633.000000 3064.500000 16.000000 9.000000 16.000000 1.000000 1.000000 1.000000 2.250000
max 1998.000000 1.0000 3.000000 1.000000 19.000000 1.000000 64.000000 1.000000 200.000000 8.000000 20.000000 1960.000000 1998.000000 3998.000000 19.000000 18.000000 20.000000 1.000000 1.000000 1.000000 3.000000
Checking columns for abnormal values

px_height¶

In [24]:
fig = px.scatter(df_train, x='px_height', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')

fig.update_layout(
    title='px_height vs Price Range (before)',
    title_x=0.5
)

fig.show()
In [25]:
df_train.px_height.describe()
Out[25]:
count    2000.000000
mean      645.108000
std       443.780811
min         0.000000
25%       282.750000
50%       564.000000
75%       947.250000
max      1960.000000
Name: px_height, dtype: float64
In [26]:
print(df_train.px_height.min())
0
In [27]:
# Remove px_hight = 0
df_train = df_train[df_train['px_height']!=0]
df_train.reset_index(inplace=True)
df_train.drop('index', axis=1, inplace=True)
In [28]:
fig = px.scatter(df_train, x='px_height', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')

fig.update_layout(
    title='px_height vs Price Range(after)',
    title_x=0.5
)

fig.show()

sc_w¶

In [29]:
fig = px.scatter(df_train, x='sc_w', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')

fig.update_layout(
    title='sc_w vs Price Range(before)',
    title_x=0.5
)

fig.show()
In [30]:
df_train.sc_w.describe()
Out[30]:
count    1998.000000
mean        5.770270
std         4.356633
min         0.000000
25%         2.000000
50%         5.000000
75%         9.000000
max        18.000000
Name: sc_w, dtype: float64
In [31]:
print(df_train.px_height.min())
1
In [32]:
# Remove sc_w =< 2
df_train = df_train[df_train['sc_w'] >= 2]
df_train.reset_index(inplace=True)
df_train.drop('index', axis=1, inplace=True)
In [33]:
fig = px.scatter(df_train, x='sc_w', y='price_range', color='price_range', marginal_y='violin', marginal_x='box')

fig.update_layout(
    title='sc_w vs Price Range(after)',
    title_x=0.5
)

fig.show()
In [34]:
df_train
Out[34]:
battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi price_range
0 842 0 2.2 0 1 0 7 0.6 188 2 2 20 756 2549 9 7 19 0 0 1 1
1 1021 1 0.5 1 0 1 53 0.7 136 3 6 905 1988 2631 17 3 7 1 1 0 2
2 563 1 0.5 1 2 1 41 0.9 145 5 6 1263 1716 2603 11 2 9 1 1 0 2
3 615 1 2.5 0 0 0 10 0.8 131 6 9 1216 1786 2769 16 8 11 1 0 0 2
4 1821 1 1.2 0 13 1 44 0.6 141 2 14 1208 1212 1411 8 2 15 1 1 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1604 858 0 2.2 0 1 0 50 0.1 84 1 2 528 1416 3978 17 16 3 1 1 0 3
1605 794 1 0.5 1 0 1 2 0.8 106 6 14 1222 1890 668 13 4 19 1 1 0 0
1606 1965 1 2.6 1 0 0 39 0.2 187 4 3 915 1965 2032 11 10 16 1 1 1 2
1607 1512 0 0.9 0 4 1 46 0.1 145 5 5 336 670 869 18 10 19 1 1 1 0
1608 510 1 2.0 1 5 1 45 0.9 168 6 16 483 754 3919 19 4 2 1 1 1 3

1609 rows × 21 columns

Apply this cahnges to the test dataframe

px_height¶

In [35]:
df_test.px_height.describe()
Out[35]:
count    1000.000000
mean      627.121000
std       432.929699
min         0.000000
25%       263.750000
50%       564.500000
75%       903.000000
max      1907.000000
Name: px_height, dtype: float64
In [36]:
print(df_test.px_height.min())
0
In [37]:
# Remove px_hight = 0
df_test = df_test[df_test['px_height']!=0]
df_test.reset_index(inplace=True)
df_test.drop('index', axis=1, inplace=True)

sc_w¶

In [38]:
df_test.sc_w.describe()
Out[38]:
count    998.000000
mean       5.315631
std        4.244245
min        0.000000
25%        2.000000
50%        5.000000
75%        8.000000
max       18.000000
Name: sc_w, dtype: float64
In [39]:
print(df_test.sc_w.min())
0
In [40]:
# Remove sc_w =< 2
df_test = df_test[df_test['sc_w'] >= 2]
df_test.reset_index(inplace=True)
df_test.drop('index', axis=1, inplace=True)
In [41]:
df_test
Out[41]:
id battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi
0 1 1043 1 1.8 1 14 0 5 0.1 193 3 16 226 1412 3476 12 7 2 0 1 0
1 3 1807 1 2.8 0 1 0 27 0.9 186 3 4 1270 1366 2396 17 10 10 0 1 1
2 5 1434 0 1.4 0 11 1 49 0.5 108 6 18 749 810 1773 15 8 7 1 0 1
3 6 1464 1 2.9 1 5 1 50 0.8 198 8 9 569 939 3506 10 7 3 1 1 1
4 7 1718 0 2.4 0 1 0 47 1.0 156 2 3 1283 1374 3873 14 2 10 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
782 994 567 1 2.7 1 14 1 56 0.4 165 8 17 555 1290 336 7 6 7 1 1 1
783 995 936 1 1.4 1 0 0 46 0.8 139 2 0 265 886 684 8 5 12 1 1 1
784 996 1700 1 1.9 0 0 1 54 0.5 170 7 17 644 913 2121 14 8 15 1 1 0
785 999 1533 1 0.5 1 0 0 50 0.4 171 2 12 38 832 2509 15 11 6 0 1 0
786 1000 1270 1 0.5 0 4 1 35 0.1 140 6 19 457 608 2828 9 2 3 1 0 1

787 rows × 21 columns

Model¶

DecisionTrees¶

In [42]:
Xd = df_train.drop('price_range', axis=1)
yd = df_train.price_range.values.reshape(-1, 1)
In [43]:
def DT(Xd, yd, test_sizes, max_depths, criterion, splitter):
    df_evaluation = pd.DataFrame(columns=['Test_size', 'Max_depth', 'Criterion', 'Splitter', 'Accuracy', 'Score'])
    for test_size in test_sizes:
        X_train, X_test, y_train, y_test = train_test_split(Xd, yd, test_size=test_size, random_state=0)
        for max_depth in max_depths:
            for c in criterion:
                for s in splitter:
                    clf = DecisionTreeClassifier(max_depth=max_depth, criterion=c, splitter=s)
                    clf.fit(X_train, y_train)
                    y_pred = clf.predict(X_test)
                    accuracy = metrics.accuracy_score(y_test, y_pred)
                    score = clf.score(Xd, yd)
                    df_evaluation = pd.concat([df_evaluation, pd.DataFrame({'Test_size': test_size,
                                                                            'Max_depth': max_depth,
                                                                            'Criterion': c,
                                                                            'Splitter': s,
                                                                            'Accuracy': accuracy,
                                                                            'Score': score}, index=[0])], ignore_index=True)


    return (df_evaluation)

def highlight_max(s):
    is_max = s == s.max()
    return ['background-color: lightgreen' if v else '' for v in is_max]

test_sizes = [0.1, 0.15, 0.2, 0.25, 0.3]
max_depths = list(range(1, 21))
criterion = ['gini', 'entropy', 'log_loss']
splitter = ['best', 'random']

df_evaluation = DT(Xd, yd, test_sizes, max_depths, criterion=criterion, splitter=splitter)
df_evaluation.style.apply(highlight_max)
Out[43]:
  Test_size Max_depth Criterion Splitter Accuracy Score
0 0.100000 1 gini best 0.521739 0.497203
1 0.100000 1 gini random 0.409938 0.442511
2 0.100000 1 entropy best 0.521739 0.497203
3 0.100000 1 entropy random 0.434783 0.461778
4 0.100000 1 log_loss best 0.521739 0.497203
5 0.100000 1 log_loss random 0.521739 0.494096
6 0.100000 2 gini best 0.776398 0.766314
7 0.100000 2 gini random 0.509317 0.552517
8 0.100000 2 entropy best 0.770186 0.766314
9 0.100000 2 entropy random 0.565217 0.597887
10 0.100000 2 log_loss best 0.770186 0.766314
11 0.100000 2 log_loss random 0.472050 0.455562
12 0.100000 3 gini best 0.788820 0.775637
13 0.100000 3 gini random 0.559006 0.582349
14 0.100000 3 entropy best 0.770186 0.766314
15 0.100000 3 entropy random 0.745342 0.729646
16 0.100000 3 log_loss best 0.770186 0.766314
17 0.100000 3 log_loss random 0.782609 0.719702
18 0.100000 4 gini best 0.838509 0.839030
19 0.100000 4 gini random 0.782609 0.770044
20 0.100000 4 entropy best 0.826087 0.817278
21 0.100000 4 entropy random 0.757764 0.769422
22 0.100000 4 log_loss best 0.826087 0.817278
23 0.100000 4 log_loss random 0.757764 0.747048
24 0.100000 5 gini best 0.813665 0.881914
25 0.100000 5 gini random 0.763975 0.768800
26 0.100000 5 entropy best 0.788820 0.874456
27 0.100000 5 entropy random 0.782609 0.789932
28 0.100000 5 log_loss best 0.788820 0.874456
29 0.100000 5 log_loss random 0.770186 0.773151
30 0.100000 6 gini best 0.826087 0.916097
31 0.100000 6 gini random 0.627329 0.691112
32 0.100000 6 entropy best 0.801242 0.907396
33 0.100000 6 entropy random 0.813665 0.806091
34 0.100000 6 log_loss best 0.801242 0.907396
35 0.100000 6 log_loss random 0.801242 0.814792
36 0.100000 7 gini best 0.819876 0.941579
37 0.100000 7 gini random 0.813665 0.870106
38 0.100000 7 entropy best 0.826087 0.945308
39 0.100000 7 entropy random 0.726708 0.745183
40 0.100000 7 log_loss best 0.832298 0.945929
41 0.100000 7 log_loss random 0.788820 0.855811
42 0.100000 8 gini best 0.832298 0.962710
43 0.100000 8 gini random 0.807453 0.856433
44 0.100000 8 entropy best 0.857143 0.972032
45 0.100000 8 entropy random 0.782609 0.835923
46 0.100000 8 log_loss best 0.813665 0.967682
47 0.100000 8 log_loss random 0.795031 0.853325
48 0.100000 9 gini best 0.844720 0.972032
49 0.100000 9 gini random 0.801242 0.896830
50 0.100000 9 entropy best 0.838509 0.978247
51 0.100000 9 entropy random 0.813665 0.924798
52 0.100000 9 log_loss best 0.850932 0.979490
53 0.100000 9 log_loss random 0.795031 0.914232
54 0.100000 10 gini best 0.838509 0.979490
55 0.100000 10 gini random 0.819876 0.957116
56 0.100000 10 entropy best 0.863354 0.985084
57 0.100000 10 entropy random 0.782609 0.935364
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455 0.250000 16 log_loss random 0.851117 0.962710
456 0.250000 17 gini best 0.816377 0.954009
457 0.250000 17 gini random 0.806452 0.951523
458 0.250000 17 entropy best 0.858561 0.964574
459 0.250000 17 entropy random 0.828784 0.957116
460 0.250000 17 log_loss best 0.851117 0.962710
461 0.250000 17 log_loss random 0.823821 0.955873
462 0.250000 18 gini best 0.784119 0.945929
463 0.250000 18 gini random 0.799007 0.949658
464 0.250000 18 entropy best 0.843672 0.960845
465 0.250000 18 entropy random 0.801489 0.950280
466 0.250000 18 log_loss best 0.851117 0.962710
467 0.250000 18 log_loss random 0.791563 0.947794
468 0.250000 19 gini best 0.803970 0.950901
469 0.250000 19 gini random 0.781638 0.945308
470 0.250000 19 entropy best 0.851117 0.962710
471 0.250000 19 entropy random 0.816377 0.954009
472 0.250000 19 log_loss best 0.843672 0.960845
473 0.250000 19 log_loss random 0.784119 0.945929
474 0.250000 20 gini best 0.816377 0.954009
475 0.250000 20 gini random 0.806452 0.951523
476 0.250000 20 entropy best 0.846154 0.961467
477 0.250000 20 entropy random 0.786600 0.946551
478 0.250000 20 log_loss best 0.861042 0.965196
479 0.250000 20 log_loss random 0.801489 0.950280
480 0.300000 1 gini best 0.496894 0.497203
481 0.300000 1 gini random 0.438923 0.459913
482 0.300000 1 entropy best 0.496894 0.497203
483 0.300000 1 entropy random 0.360248 0.397141
484 0.300000 1 log_loss best 0.496894 0.497203
485 0.300000 1 log_loss random 0.279503 0.305780
486 0.300000 2 gini best 0.726708 0.756992
487 0.300000 2 gini random 0.716356 0.731510
488 0.300000 2 entropy best 0.730849 0.758235
489 0.300000 2 entropy random 0.548654 0.551896
490 0.300000 2 log_loss best 0.730849 0.758235
491 0.300000 2 log_loss random 0.540373 0.523928
492 0.300000 3 gini best 0.726708 0.761342
493 0.300000 3 gini random 0.507246 0.539466
494 0.300000 3 entropy best 0.730849 0.758235
495 0.300000 3 entropy random 0.681159 0.703543
496 0.300000 3 log_loss best 0.730849 0.758235
497 0.300000 3 log_loss random 0.658385 0.676818
498 0.300000 4 gini best 0.801242 0.822871
499 0.300000 4 gini random 0.772257 0.768179
500 0.300000 4 entropy best 0.803313 0.817278
501 0.300000 4 entropy random 0.693582 0.696706
502 0.300000 4 log_loss best 0.803313 0.817278
503 0.300000 4 log_loss random 0.701863 0.732753
504 0.300000 5 gini best 0.842650 0.881293
505 0.300000 5 gini random 0.772257 0.783717
506 0.300000 5 entropy best 0.826087 0.866998
507 0.300000 5 entropy random 0.743271 0.766936
508 0.300000 5 log_loss best 0.826087 0.866998
509 0.300000 5 log_loss random 0.660455 0.668117
510 0.300000 6 gini best 0.842650 0.908017
511 0.300000 6 gini random 0.792961 0.815413
512 0.300000 6 entropy best 0.828157 0.893723
513 0.300000 6 entropy random 0.776398 0.836544
514 0.300000 6 log_loss best 0.826087 0.893101
515 0.300000 6 log_loss random 0.693582 0.741454
516 0.300000 7 gini best 0.819876 0.921069
517 0.300000 7 gini random 0.819876 0.875699
518 0.300000 7 entropy best 0.848861 0.932256
519 0.300000 7 entropy random 0.792961 0.855190
520 0.300000 7 log_loss best 0.855072 0.934121
521 0.300000 7 log_loss random 0.790890 0.821628
522 0.300000 8 gini best 0.850932 0.947172
523 0.300000 8 gini random 0.807453 0.852082
524 0.300000 8 entropy best 0.817805 0.935364
525 0.300000 8 entropy random 0.780538 0.825357
526 0.300000 8 log_loss best 0.817805 0.935364
527 0.300000 8 log_loss random 0.797101 0.829086
528 0.300000 9 gini best 0.826087 0.945308
529 0.300000 9 gini random 0.799172 0.875078
530 0.300000 9 entropy best 0.826087 0.947172
531 0.300000 9 entropy random 0.784679 0.893723
532 0.300000 9 log_loss best 0.838509 0.951523
533 0.300000 9 log_loss random 0.817805 0.906153
534 0.300000 10 gini best 0.815735 0.944065
535 0.300000 10 gini random 0.778468 0.890615
536 0.300000 10 entropy best 0.830228 0.949037
537 0.300000 10 entropy random 0.774327 0.880671
538 0.300000 10 log_loss best 0.828157 0.948415
539 0.300000 10 log_loss random 0.850932 0.919826
540 0.300000 11 gini best 0.821946 0.946551
541 0.300000 11 gini random 0.807453 0.930392
542 0.300000 11 entropy best 0.836439 0.950901
543 0.300000 11 entropy random 0.824017 0.930392
544 0.300000 11 log_loss best 0.848861 0.954630
545 0.300000 11 log_loss random 0.778468 0.906774
546 0.300000 12 gini best 0.830228 0.949037
547 0.300000 12 gini random 0.776398 0.932878
548 0.300000 12 entropy best 0.840580 0.952144
549 0.300000 12 entropy random 0.813665 0.937850
550 0.300000 12 log_loss best 0.848861 0.954630
551 0.300000 12 log_loss random 0.824017 0.939714
552 0.300000 13 gini best 0.828157 0.948415
553 0.300000 13 gini random 0.778468 0.933499
554 0.300000 13 entropy best 0.828157 0.948415
555 0.300000 13 entropy random 0.809524 0.938471
556 0.300000 13 log_loss best 0.828157 0.948415
557 0.300000 13 log_loss random 0.797101 0.936607
558 0.300000 14 gini best 0.828157 0.948415
559 0.300000 14 gini random 0.768116 0.929770
560 0.300000 14 entropy best 0.834369 0.950280
561 0.300000 14 entropy random 0.836439 0.950280
562 0.300000 14 log_loss best 0.834369 0.950280
563 0.300000 14 log_loss random 0.809524 0.942822
564 0.300000 15 gini best 0.838509 0.951523
565 0.300000 15 gini random 0.778468 0.920447
566 0.300000 15 entropy best 0.844720 0.953387
567 0.300000 15 entropy random 0.824017 0.942822
568 0.300000 15 log_loss best 0.834369 0.950280
569 0.300000 15 log_loss random 0.795031 0.938471
570 0.300000 16 gini best 0.819876 0.945929
571 0.300000 16 gini random 0.788820 0.936607
572 0.300000 16 entropy best 0.826087 0.947794
573 0.300000 16 entropy random 0.819876 0.945308
574 0.300000 16 log_loss best 0.832298 0.949658
575 0.300000 16 log_loss random 0.778468 0.933499
576 0.300000 17 gini best 0.803313 0.940957
577 0.300000 17 gini random 0.797101 0.939093
578 0.300000 17 entropy best 0.830228 0.949037
579 0.300000 17 entropy random 0.809524 0.942822
580 0.300000 17 log_loss best 0.838509 0.951523
581 0.300000 17 log_loss random 0.788820 0.936607
582 0.300000 18 gini best 0.830228 0.949037
583 0.300000 18 gini random 0.786749 0.935985
584 0.300000 18 entropy best 0.834369 0.950280
585 0.300000 18 entropy random 0.809524 0.942822
586 0.300000 18 log_loss best 0.842650 0.952766
587 0.300000 18 log_loss random 0.801242 0.940336
588 0.300000 19 gini best 0.811594 0.943443
589 0.300000 19 gini random 0.795031 0.938471
590 0.300000 19 entropy best 0.836439 0.950901
591 0.300000 19 entropy random 0.815735 0.944686
592 0.300000 19 log_loss best 0.853002 0.955873
593 0.300000 19 log_loss random 0.784679 0.935364
594 0.300000 20 gini best 0.813665 0.944065
595 0.300000 20 gini random 0.809524 0.942822
596 0.300000 20 entropy best 0.832298 0.949658
597 0.300000 20 entropy random 0.809524 0.942822
598 0.300000 20 log_loss best 0.840580 0.952144
599 0.300000 20 log_loss random 0.836439 0.950901
Row:60, testsize=0.1, Max_depth=11, Criterion=gini, Splitter=best, Accuracy=0.881988, Score=0.986948
In [44]:
X_train, X_test, y_train, y_test = train_test_split(Xd, yd, test_size=0.1, random_state=0)
In [45]:
clf_dts = DecisionTreeClassifier(max_depth=11, criterion='gini', splitter='best')
clf_dts.fit(X_train, y_train)
y_pred = clf_dts.predict(X_test)
In [46]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_dts.score(Xd, yd))
Accuracy =  0.8757763975155279 
Score =  0.9863269111249223

RandomForest¶

In [47]:
Xr = df_train.drop('price_range', axis=1)
yr = df_train.price_range.values.reshape(-1, 1)
In [48]:
def RF(Xr, yr, Testsize, mdepth):
    df_evaluation = pd.DataFrame()
    for x in Testsize:
        X_train, X_test, y_train, y_test = train_test_split(Xr, yr, test_size=x, random_state=0)
        for maxdepth in mdepth:
            for n_estimators in [10, 50, 100, 200, 300]:
                for criterion in ["gini", "entropy", "log_loss"]:
                        RF = RandomForestClassifier(n_estimators=n_estimators,
                                                     criterion=criterion,
                                                     max_depth=maxdepth)
                        RF.fit(X_train, y_train)
                        y_pred = RF.predict(X_test)
                        dict = {'Test_size': x,
                                "Max_depth": maxdepth,
                                "n_estimators": n_estimators,
                                "criterion": criterion,
                                'acc': metrics.accuracy_score(y_test,y_pred),
                                "score": RF.score(Xr, yr)}
                        df_evaluation = pd.concat([df_evaluation, pd.DataFrame(dict, index=[0])], ignore_index=True)
    return(df_evaluation)


df_evaluation = RF(Xr, yr, [.1,.15,.2,.25,.3], range(1,21))
df_evaluation.style.apply(highlight_max)
Out[48]:
  Test_size Max_depth n_estimators criterion acc score
0 0.100000 1 10 gini 0.565217 0.533872
1 0.100000 1 10 entropy 0.527950 0.523306
2 0.100000 1 10 log_loss 0.527950 0.528278
3 0.100000 1 50 gini 0.621118 0.628341
4 0.100000 1 50 entropy 0.540373 0.563704
5 0.100000 1 50 log_loss 0.614907 0.576756
6 0.100000 1 100 gini 0.670807 0.679304
7 0.100000 1 100 entropy 0.596273 0.582971
8 0.100000 1 100 log_loss 0.590062 0.566812
9 0.100000 1 200 gini 0.677019 0.666252
10 0.100000 1 200 entropy 0.602484 0.587321
11 0.100000 1 200 log_loss 0.590062 0.589807
12 0.100000 1 300 gini 0.664596 0.696085
13 0.100000 1 300 entropy 0.608696 0.576756
14 0.100000 1 300 log_loss 0.621118 0.587321
15 0.100000 2 10 gini 0.658385 0.679925
16 0.100000 2 10 entropy 0.658385 0.663145
17 0.100000 2 10 log_loss 0.571429 0.575513
18 0.100000 2 50 gini 0.726708 0.791175
19 0.100000 2 50 entropy 0.801242 0.805469
20 0.100000 2 50 log_loss 0.720497 0.727781
21 0.100000 2 100 gini 0.739130 0.764450
22 0.100000 2 100 entropy 0.664596 0.690491
23 0.100000 2 100 log_loss 0.664596 0.717837
24 0.100000 2 200 gini 0.763975 0.795525
25 0.100000 2 200 entropy 0.726708 0.740211
26 0.100000 2 200 log_loss 0.708075 0.701057
27 0.100000 2 300 gini 0.782609 0.796147
28 0.100000 2 300 entropy 0.739130 0.712865
29 0.100000 2 300 log_loss 0.739130 0.753263
30 0.100000 3 10 gini 0.689441 0.755749
31 0.100000 3 10 entropy 0.596273 0.646364
32 0.100000 3 10 log_loss 0.695652 0.721566
33 0.100000 3 50 gini 0.813665 0.825979
34 0.100000 3 50 entropy 0.807453 0.812927
35 0.100000 3 50 log_loss 0.807453 0.829708
36 0.100000 3 100 gini 0.807453 0.837787
37 0.100000 3 100 entropy 0.813665 0.841516
38 0.100000 3 100 log_loss 0.745342 0.796147
39 0.100000 3 200 gini 0.788820 0.842138
40 0.100000 3 200 entropy 0.788820 0.837166
41 0.100000 3 200 log_loss 0.782609 0.819142
42 0.100000 3 300 gini 0.832298 0.852704
43 0.100000 3 300 entropy 0.782609 0.835301
44 0.100000 3 300 log_loss 0.819876 0.844624
45 0.100000 4 10 gini 0.708075 0.764450
46 0.100000 4 10 entropy 0.726708 0.798633
47 0.100000 4 10 log_loss 0.788820 0.831572
48 0.100000 4 50 gini 0.819876 0.882536
49 0.100000 4 50 entropy 0.782609 0.845245
50 0.100000 4 50 log_loss 0.819876 0.862648
51 0.100000 4 100 gini 0.813665 0.884400
52 0.100000 4 100 entropy 0.844720 0.867620
53 0.100000 4 100 log_loss 0.788820 0.867620
54 0.100000 4 200 gini 0.807453 0.889994
55 0.100000 4 200 entropy 0.838509 0.880050
56 0.100000 4 200 log_loss 0.795031 0.865134
57 0.100000 4 300 gini 0.819876 0.881914
58 0.100000 4 300 entropy 0.807453 0.870727
59 0.100000 4 300 log_loss 0.826087 0.871349
60 0.100000 5 10 gini 0.832298 0.885022
61 0.100000 5 10 entropy 0.757764 0.821007
62 0.100000 5 10 log_loss 0.782609 0.852704
63 0.100000 5 50 gini 0.869565 0.908639
64 0.100000 5 50 entropy 0.832298 0.901181
65 0.100000 5 50 log_loss 0.801242 0.887508
66 0.100000 5 100 gini 0.850932 0.917340
67 0.100000 5 100 entropy 0.813665 0.899938
68 0.100000 5 100 log_loss 0.832298 0.897452
69 0.100000 5 200 gini 0.838509 0.916718
70 0.100000 5 200 entropy 0.838509 0.908017
71 0.100000 5 200 log_loss 0.844720 0.908017
72 0.100000 5 300 gini 0.850932 0.925420
73 0.100000 5 300 entropy 0.838509 0.905531
74 0.100000 5 300 log_loss 0.832298 0.906774
75 0.100000 6 10 gini 0.807453 0.876321
76 0.100000 6 10 entropy 0.801242 0.901181
77 0.100000 6 10 log_loss 0.813665 0.896209
78 0.100000 6 50 gini 0.875776 0.947172
79 0.100000 6 50 entropy 0.819876 0.927284
80 0.100000 6 50 log_loss 0.819876 0.929770
81 0.100000 6 100 gini 0.894410 0.950901
82 0.100000 6 100 entropy 0.875776 0.942200
83 0.100000 6 100 log_loss 0.832298 0.934121
84 0.100000 6 200 gini 0.863354 0.944065
85 0.100000 6 200 entropy 0.844720 0.935985
86 0.100000 6 200 log_loss 0.850932 0.941579
87 0.100000 6 300 gini 0.881988 0.945308
88 0.100000 6 300 entropy 0.857143 0.943443
89 0.100000 6 300 log_loss 0.850932 0.943443
90 0.100000 7 10 gini 0.850932 0.935985
91 0.100000 7 10 entropy 0.850932 0.929149
92 0.100000 7 10 log_loss 0.739130 0.914232
93 0.100000 7 50 gini 0.881988 0.959602
94 0.100000 7 50 entropy 0.857143 0.970789
95 0.100000 7 50 log_loss 0.881988 0.969546
96 0.100000 7 100 gini 0.900621 0.965817
97 0.100000 7 100 entropy 0.857143 0.964574
98 0.100000 7 100 log_loss 0.869565 0.967682
99 0.100000 7 200 gini 0.863354 0.965196
100 0.100000 7 200 entropy 0.850932 0.967060
101 0.100000 7 200 log_loss 0.850932 0.968925
102 0.100000 7 300 gini 0.869565 0.967060
103 0.100000 7 300 entropy 0.869565 0.969546
104 0.100000 7 300 log_loss 0.863354 0.968925
105 0.100000 8 10 gini 0.813665 0.943443
106 0.100000 8 10 entropy 0.838509 0.956495
107 0.100000 8 10 log_loss 0.857143 0.949037
108 0.100000 8 50 gini 0.869565 0.971411
109 0.100000 8 50 entropy 0.844720 0.977004
110 0.100000 8 50 log_loss 0.888199 0.983841
111 0.100000 8 100 gini 0.857143 0.978869
112 0.100000 8 100 entropy 0.857143 0.980733
113 0.100000 8 100 log_loss 0.857143 0.978869
114 0.100000 8 200 gini 0.881988 0.981976
115 0.100000 8 200 entropy 0.869565 0.984462
116 0.100000 8 200 log_loss 0.875776 0.981355
117 0.100000 8 300 gini 0.894410 0.982598
118 0.100000 8 300 entropy 0.857143 0.983219
119 0.100000 8 300 log_loss 0.857143 0.982598
120 0.100000 9 10 gini 0.838509 0.965817
121 0.100000 9 10 entropy 0.782609 0.967682
122 0.100000 9 10 log_loss 0.770186 0.960845
123 0.100000 9 50 gini 0.900621 0.986327
124 0.100000 9 50 entropy 0.850932 0.982598
125 0.100000 9 50 log_loss 0.888199 0.985705
126 0.100000 9 100 gini 0.900621 0.984462
127 0.100000 9 100 entropy 0.875776 0.985705
128 0.100000 9 100 log_loss 0.875776 0.986327
129 0.100000 9 200 gini 0.900621 0.985705
130 0.100000 9 200 entropy 0.894410 0.988813
131 0.100000 9 200 log_loss 0.900621 0.988813
132 0.100000 9 300 gini 0.906832 0.988813
133 0.100000 9 300 entropy 0.875776 0.987570
134 0.100000 9 300 log_loss 0.900621 0.990056
135 0.100000 10 10 gini 0.813665 0.968303
136 0.100000 10 10 entropy 0.801242 0.970168
137 0.100000 10 10 log_loss 0.819876 0.967682
138 0.100000 10 50 gini 0.906832 0.990677
139 0.100000 10 50 entropy 0.888199 0.988813
140 0.100000 10 50 log_loss 0.888199 0.986948
141 0.100000 10 100 gini 0.925466 0.991920
142 0.100000 10 100 entropy 0.881988 0.988191
143 0.100000 10 100 log_loss 0.888199 0.988191
144 0.100000 10 200 gini 0.900621 0.988813
145 0.100000 10 200 entropy 0.900621 0.990056
146 0.100000 10 200 log_loss 0.881988 0.988191
147 0.100000 10 300 gini 0.913043 0.991299
148 0.100000 10 300 entropy 0.869565 0.986948
149 0.100000 10 300 log_loss 0.875776 0.987570
150 0.100000 11 10 gini 0.838509 0.972032
151 0.100000 11 10 entropy 0.776398 0.971411
152 0.100000 11 10 log_loss 0.795031 0.975140
153 0.100000 11 50 gini 0.875776 0.987570
154 0.100000 11 50 entropy 0.869565 0.986948
155 0.100000 11 50 log_loss 0.863354 0.986327
156 0.100000 11 100 gini 0.881988 0.988191
157 0.100000 11 100 entropy 0.881988 0.988191
158 0.100000 11 100 log_loss 0.906832 0.990677
159 0.100000 11 200 gini 0.900621 0.990056
160 0.100000 11 200 entropy 0.894410 0.989434
161 0.100000 11 200 log_loss 0.894410 0.989434
162 0.100000 11 300 gini 0.900621 0.990056
163 0.100000 11 300 entropy 0.900621 0.990056
164 0.100000 11 300 log_loss 0.888199 0.988813
165 0.100000 12 10 gini 0.782609 0.973897
166 0.100000 12 10 entropy 0.850932 0.979490
167 0.100000 12 10 log_loss 0.832298 0.975761
168 0.100000 12 50 gini 0.881988 0.988191
169 0.100000 12 50 entropy 0.888199 0.988813
170 0.100000 12 50 log_loss 0.875776 0.987570
171 0.100000 12 100 gini 0.894410 0.989434
172 0.100000 12 100 entropy 0.863354 0.986327
173 0.100000 12 100 log_loss 0.863354 0.986327
174 0.100000 12 200 gini 0.906832 0.990677
175 0.100000 12 200 entropy 0.881988 0.988191
176 0.100000 12 200 log_loss 0.857143 0.985705
177 0.100000 12 300 gini 0.894410 0.989434
178 0.100000 12 300 entropy 0.888199 0.988813
179 0.100000 12 300 log_loss 0.875776 0.987570
180 0.100000 13 10 gini 0.757764 0.972032
181 0.100000 13 10 entropy 0.819876 0.976383
182 0.100000 13 10 log_loss 0.776398 0.974518
183 0.100000 13 50 gini 0.875776 0.987570
184 0.100000 13 50 entropy 0.894410 0.989434
185 0.100000 13 50 log_loss 0.894410 0.989434
186 0.100000 13 100 gini 0.888199 0.988813
187 0.100000 13 100 entropy 0.906832 0.990677
188 0.100000 13 100 log_loss 0.875776 0.987570
189 0.100000 13 200 gini 0.900621 0.990056
190 0.100000 13 200 entropy 0.881988 0.988191
191 0.100000 13 200 log_loss 0.906832 0.990677
192 0.100000 13 300 gini 0.913043 0.991299
193 0.100000 13 300 entropy 0.894410 0.989434
194 0.100000 13 300 log_loss 0.875776 0.987570
195 0.100000 14 10 gini 0.857143 0.980733
196 0.100000 14 10 entropy 0.807453 0.973275
197 0.100000 14 10 log_loss 0.782609 0.974518
198 0.100000 14 50 gini 0.919255 0.991920
199 0.100000 14 50 entropy 0.869565 0.986948
200 0.100000 14 50 log_loss 0.863354 0.986327
201 0.100000 14 100 gini 0.900621 0.990056
202 0.100000 14 100 entropy 0.875776 0.987570
203 0.100000 14 100 log_loss 0.900621 0.990056
204 0.100000 14 200 gini 0.919255 0.991920
205 0.100000 14 200 entropy 0.906832 0.990677
206 0.100000 14 200 log_loss 0.888199 0.988813
207 0.100000 14 300 gini 0.894410 0.989434
208 0.100000 14 300 entropy 0.894410 0.989434
209 0.100000 14 300 log_loss 0.894410 0.989434
210 0.100000 15 10 gini 0.826087 0.980733
211 0.100000 15 10 entropy 0.832298 0.980112
212 0.100000 15 10 log_loss 0.832298 0.980733
213 0.100000 15 50 gini 0.906832 0.990677
214 0.100000 15 50 entropy 0.894410 0.989434
215 0.100000 15 50 log_loss 0.857143 0.985705
216 0.100000 15 100 gini 0.931677 0.993163
217 0.100000 15 100 entropy 0.875776 0.987570
218 0.100000 15 100 log_loss 0.894410 0.989434
219 0.100000 15 200 gini 0.913043 0.991299
220 0.100000 15 200 entropy 0.875776 0.987570
221 0.100000 15 200 log_loss 0.875776 0.987570
222 0.100000 15 300 gini 0.900621 0.990056
223 0.100000 15 300 entropy 0.881988 0.988191
224 0.100000 15 300 log_loss 0.888199 0.988813
225 0.100000 16 10 gini 0.826087 0.977626
226 0.100000 16 10 entropy 0.732919 0.972654
227 0.100000 16 10 log_loss 0.844720 0.981976
228 0.100000 16 50 gini 0.919255 0.991920
229 0.100000 16 50 entropy 0.857143 0.985705
230 0.100000 16 50 log_loss 0.906832 0.990677
231 0.100000 16 100 gini 0.913043 0.991299
232 0.100000 16 100 entropy 0.900621 0.990056
233 0.100000 16 100 log_loss 0.881988 0.988191
234 0.100000 16 200 gini 0.900621 0.990056
235 0.100000 16 200 entropy 0.881988 0.988191
236 0.100000 16 200 log_loss 0.869565 0.986948
237 0.100000 16 300 gini 0.919255 0.991920
238 0.100000 16 300 entropy 0.888199 0.988813
239 0.100000 16 300 log_loss 0.888199 0.988813
240 0.100000 17 10 gini 0.881988 0.982598
241 0.100000 17 10 entropy 0.826087 0.977004
242 0.100000 17 10 log_loss 0.826087 0.980112
243 0.100000 17 50 gini 0.906832 0.990677
244 0.100000 17 50 entropy 0.850932 0.985084
245 0.100000 17 50 log_loss 0.875776 0.987570
246 0.100000 17 100 gini 0.919255 0.991920
247 0.100000 17 100 entropy 0.888199 0.988813
248 0.100000 17 100 log_loss 0.875776 0.987570
249 0.100000 17 200 gini 0.888199 0.988813
250 0.100000 17 200 entropy 0.888199 0.988813
251 0.100000 17 200 log_loss 0.875776 0.987570
252 0.100000 17 300 gini 0.888199 0.988813
253 0.100000 17 300 entropy 0.900621 0.990056
254 0.100000 17 300 log_loss 0.869565 0.986948
255 0.100000 18 10 gini 0.801242 0.977626
256 0.100000 18 10 entropy 0.826087 0.977004
257 0.100000 18 10 log_loss 0.826087 0.980733
258 0.100000 18 50 gini 0.913043 0.991299
259 0.100000 18 50 entropy 0.863354 0.986327
260 0.100000 18 50 log_loss 0.913043 0.991299
261 0.100000 18 100 gini 0.894410 0.989434
262 0.100000 18 100 entropy 0.863354 0.986327
263 0.100000 18 100 log_loss 0.875776 0.987570
264 0.100000 18 200 gini 0.894410 0.989434
265 0.100000 18 200 entropy 0.888199 0.988813
266 0.100000 18 200 log_loss 0.875776 0.987570
267 0.100000 18 300 gini 0.900621 0.990056
268 0.100000 18 300 entropy 0.888199 0.988813
269 0.100000 18 300 log_loss 0.900621 0.990056
270 0.100000 19 10 gini 0.770186 0.973275
271 0.100000 19 10 entropy 0.819876 0.978247
272 0.100000 19 10 log_loss 0.826087 0.980733
273 0.100000 19 50 gini 0.869565 0.986948
274 0.100000 19 50 entropy 0.894410 0.989434
275 0.100000 19 50 log_loss 0.863354 0.986327
276 0.100000 19 100 gini 0.894410 0.989434
277 0.100000 19 100 entropy 0.900621 0.990056
278 0.100000 19 100 log_loss 0.894410 0.989434
279 0.100000 19 200 gini 0.888199 0.988813
280 0.100000 19 200 entropy 0.894410 0.989434
281 0.100000 19 200 log_loss 0.900621 0.990056
282 0.100000 19 300 gini 0.906832 0.990677
283 0.100000 19 300 entropy 0.869565 0.986948
284 0.100000 19 300 log_loss 0.906832 0.990677
285 0.100000 20 10 gini 0.776398 0.971411
286 0.100000 20 10 entropy 0.763975 0.974518
287 0.100000 20 10 log_loss 0.763975 0.975761
288 0.100000 20 50 gini 0.906832 0.990677
289 0.100000 20 50 entropy 0.857143 0.985705
290 0.100000 20 50 log_loss 0.857143 0.985705
291 0.100000 20 100 gini 0.900621 0.990056
292 0.100000 20 100 entropy 0.869565 0.986948
293 0.100000 20 100 log_loss 0.875776 0.987570
294 0.100000 20 200 gini 0.925466 0.992542
295 0.100000 20 200 entropy 0.906832 0.990677
296 0.100000 20 200 log_loss 0.900621 0.990056
297 0.100000 20 300 gini 0.900621 0.990056
298 0.100000 20 300 entropy 0.881988 0.988191
299 0.100000 20 300 log_loss 0.900621 0.990056
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301 0.150000 1 10 entropy 0.578512 0.557489
302 0.150000 1 10 log_loss 0.578512 0.560597
303 0.150000 1 50 gini 0.710744 0.681790
304 0.150000 1 50 entropy 0.615702 0.590429
305 0.150000 1 50 log_loss 0.615702 0.584835
306 0.150000 1 100 gini 0.615702 0.651958
307 0.150000 1 100 entropy 0.582645 0.588564
308 0.150000 1 100 log_loss 0.603306 0.577377
309 0.150000 1 200 gini 0.652893 0.669360
310 0.150000 1 200 entropy 0.607438 0.581106
311 0.150000 1 200 log_loss 0.586777 0.569919
312 0.150000 1 300 gini 0.681818 0.677439
313 0.150000 1 300 entropy 0.603306 0.595401
314 0.150000 1 300 log_loss 0.595041 0.578620
315 0.150000 2 10 gini 0.681818 0.743319
316 0.150000 2 10 entropy 0.504132 0.541952
317 0.150000 2 10 log_loss 0.524793 0.550031
318 0.150000 2 50 gini 0.760331 0.764450
319 0.150000 2 50 entropy 0.747934 0.766314
320 0.150000 2 50 log_loss 0.681818 0.715351
321 0.150000 2 100 gini 0.739669 0.749534
322 0.150000 2 100 entropy 0.706612 0.711001
323 0.150000 2 100 log_loss 0.702479 0.720323
324 0.150000 2 200 gini 0.818182 0.825979
325 0.150000 2 200 entropy 0.743802 0.748912
326 0.150000 2 200 log_loss 0.776860 0.765071
327 0.150000 2 300 gini 0.789256 0.809820
328 0.150000 2 300 entropy 0.719008 0.704786
329 0.150000 2 300 log_loss 0.723140 0.737104
330 0.150000 3 10 gini 0.768595 0.788689
331 0.150000 3 10 entropy 0.735537 0.717837
332 0.150000 3 10 log_loss 0.735537 0.732132
333 0.150000 3 50 gini 0.826446 0.832194
334 0.150000 3 50 entropy 0.752066 0.759478
335 0.150000 3 50 log_loss 0.801653 0.815413
336 0.150000 3 100 gini 0.822314 0.860162
337 0.150000 3 100 entropy 0.805785 0.823493
338 0.150000 3 100 log_loss 0.776860 0.823493
339 0.150000 3 200 gini 0.838843 0.852082
340 0.150000 3 200 entropy 0.805785 0.831572
341 0.150000 3 200 log_loss 0.809917 0.831572
342 0.150000 3 300 gini 0.822314 0.850218
343 0.150000 3 300 entropy 0.801653 0.837166
344 0.150000 3 300 log_loss 0.818182 0.848353
345 0.150000 4 10 gini 0.760331 0.793661
346 0.150000 4 10 entropy 0.826446 0.832194
347 0.150000 4 10 log_loss 0.814050 0.828465
348 0.150000 4 50 gini 0.834711 0.875078
349 0.150000 4 50 entropy 0.814050 0.858919
350 0.150000 4 50 log_loss 0.826446 0.868241
351 0.150000 4 100 gini 0.822314 0.882536
352 0.150000 4 100 entropy 0.818182 0.870106
353 0.150000 4 100 log_loss 0.818182 0.869484
354 0.150000 4 200 gini 0.838843 0.893723
355 0.150000 4 200 entropy 0.826446 0.876942
356 0.150000 4 200 log_loss 0.822314 0.875699
357 0.150000 4 300 gini 0.830579 0.881293
358 0.150000 4 300 entropy 0.830579 0.871349
359 0.150000 4 300 log_loss 0.830579 0.876942
360 0.150000 5 10 gini 0.776860 0.843381
361 0.150000 5 10 entropy 0.780992 0.865134
362 0.150000 5 10 log_loss 0.756198 0.814792
363 0.150000 5 50 gini 0.834711 0.904288
364 0.150000 5 50 entropy 0.826446 0.887508
365 0.150000 5 50 log_loss 0.822314 0.896209
366 0.150000 5 100 gini 0.867769 0.918583
367 0.150000 5 100 entropy 0.826446 0.896830
368 0.150000 5 100 log_loss 0.842975 0.903045
369 0.150000 5 200 gini 0.847107 0.916718
370 0.150000 5 200 entropy 0.814050 0.901802
371 0.150000 5 200 log_loss 0.826446 0.899938
372 0.150000 5 300 gini 0.859504 0.920447
373 0.150000 5 300 entropy 0.830579 0.898073
374 0.150000 5 300 log_loss 0.838843 0.900559
375 0.150000 6 10 gini 0.809917 0.888129
376 0.150000 6 10 entropy 0.797521 0.889372
377 0.150000 6 10 log_loss 0.780992 0.900559
378 0.150000 6 50 gini 0.871901 0.936607
379 0.150000 6 50 entropy 0.851240 0.940336
380 0.150000 6 50 log_loss 0.859504 0.932256
381 0.150000 6 100 gini 0.847107 0.934121
382 0.150000 6 100 entropy 0.855372 0.934121
383 0.150000 6 100 log_loss 0.851240 0.935985
384 0.150000 6 200 gini 0.876033 0.945929
385 0.150000 6 200 entropy 0.855372 0.932256
386 0.150000 6 200 log_loss 0.851240 0.937850
387 0.150000 6 300 gini 0.859504 0.944686
388 0.150000 6 300 entropy 0.855372 0.935985
389 0.150000 6 300 log_loss 0.867769 0.939714
390 0.150000 7 10 gini 0.814050 0.913611
391 0.150000 7 10 entropy 0.809917 0.931013
392 0.150000 7 10 log_loss 0.863636 0.927906
393 0.150000 7 50 gini 0.867769 0.954630
394 0.150000 7 50 entropy 0.855372 0.954630
395 0.150000 7 50 log_loss 0.851240 0.955252
396 0.150000 7 100 gini 0.871901 0.966439
397 0.150000 7 100 entropy 0.842975 0.959602
398 0.150000 7 100 log_loss 0.863636 0.960845
399 0.150000 7 200 gini 0.888430 0.965196
400 0.150000 7 200 entropy 0.871901 0.969546
401 0.150000 7 200 log_loss 0.863636 0.958981
402 0.150000 7 300 gini 0.867769 0.962088
403 0.150000 7 300 entropy 0.871901 0.964574
404 0.150000 7 300 log_loss 0.851240 0.961467
405 0.150000 8 10 gini 0.859504 0.954009
406 0.150000 8 10 entropy 0.772727 0.937850
407 0.150000 8 10 log_loss 0.756198 0.919204
408 0.150000 8 50 gini 0.871901 0.974518
409 0.150000 8 50 entropy 0.855372 0.972032
410 0.150000 8 50 log_loss 0.880165 0.972654
411 0.150000 8 100 gini 0.863636 0.971411
412 0.150000 8 100 entropy 0.855372 0.970789
413 0.150000 8 100 log_loss 0.884298 0.979490
414 0.150000 8 200 gini 0.900826 0.979490
415 0.150000 8 200 entropy 0.876033 0.975140
416 0.150000 8 200 log_loss 0.855372 0.974518
417 0.150000 8 300 gini 0.876033 0.974518
418 0.150000 8 300 entropy 0.859504 0.977004
419 0.150000 8 300 log_loss 0.867769 0.977626
420 0.150000 9 10 gini 0.814050 0.949037
421 0.150000 9 10 entropy 0.859504 0.961467
422 0.150000 9 10 log_loss 0.809917 0.960224
423 0.150000 9 50 gini 0.859504 0.973897
424 0.150000 9 50 entropy 0.863636 0.977004
425 0.150000 9 50 log_loss 0.925620 0.988191
426 0.150000 9 100 gini 0.896694 0.981976
427 0.150000 9 100 entropy 0.855372 0.977004
428 0.150000 9 100 log_loss 0.880165 0.981976
429 0.150000 9 200 gini 0.888430 0.981355
430 0.150000 9 200 entropy 0.880165 0.981976
431 0.150000 9 200 log_loss 0.863636 0.978869
432 0.150000 9 300 gini 0.871901 0.979490
433 0.150000 9 300 entropy 0.855372 0.977626
434 0.150000 9 300 log_loss 0.876033 0.980733
435 0.150000 10 10 gini 0.772727 0.955873
436 0.150000 10 10 entropy 0.830579 0.968925
437 0.150000 10 10 log_loss 0.809917 0.961467
438 0.150000 10 50 gini 0.867769 0.979490
439 0.150000 10 50 entropy 0.876033 0.981355
440 0.150000 10 50 log_loss 0.892562 0.983841
441 0.150000 10 100 gini 0.888430 0.982598
442 0.150000 10 100 entropy 0.888430 0.983219
443 0.150000 10 100 log_loss 0.863636 0.979490
444 0.150000 10 200 gini 0.880165 0.981976
445 0.150000 10 200 entropy 0.880165 0.981976
446 0.150000 10 200 log_loss 0.900826 0.985084
447 0.150000 10 300 gini 0.880165 0.981355
448 0.150000 10 300 entropy 0.880165 0.981976
449 0.150000 10 300 log_loss 0.888430 0.983219
450 0.150000 11 10 gini 0.859504 0.965817
451 0.150000 11 10 entropy 0.830579 0.969546
452 0.150000 11 10 log_loss 0.822314 0.964574
453 0.150000 11 50 gini 0.876033 0.981355
454 0.150000 11 50 entropy 0.867769 0.980112
455 0.150000 11 50 log_loss 0.888430 0.983219
456 0.150000 11 100 gini 0.917355 0.987570
457 0.150000 11 100 entropy 0.888430 0.983219
458 0.150000 11 100 log_loss 0.888430 0.983219
459 0.150000 11 200 gini 0.904959 0.985705
460 0.150000 11 200 entropy 0.876033 0.981355
461 0.150000 11 200 log_loss 0.896694 0.984462
462 0.150000 11 300 gini 0.892562 0.983841
463 0.150000 11 300 entropy 0.880165 0.981976
464 0.150000 11 300 log_loss 0.888430 0.983219
465 0.150000 12 10 gini 0.818182 0.970789
466 0.150000 12 10 entropy 0.863636 0.975761
467 0.150000 12 10 log_loss 0.863636 0.973897
468 0.150000 12 50 gini 0.884298 0.982598
469 0.150000 12 50 entropy 0.867769 0.980112
470 0.150000 12 50 log_loss 0.855372 0.978247
471 0.150000 12 100 gini 0.871901 0.980733
472 0.150000 12 100 entropy 0.904959 0.985705
473 0.150000 12 100 log_loss 0.880165 0.981976
474 0.150000 12 200 gini 0.892562 0.983841
475 0.150000 12 200 entropy 0.888430 0.983219
476 0.150000 12 200 log_loss 0.884298 0.982598
477 0.150000 12 300 gini 0.896694 0.984462
478 0.150000 12 300 entropy 0.892562 0.983841
479 0.150000 12 300 log_loss 0.871901 0.980733
480 0.150000 13 10 gini 0.847107 0.973897
481 0.150000 13 10 entropy 0.818182 0.968925
482 0.150000 13 10 log_loss 0.801653 0.963331
483 0.150000 13 50 gini 0.900826 0.985084
484 0.150000 13 50 entropy 0.863636 0.979490
485 0.150000 13 50 log_loss 0.892562 0.983841
486 0.150000 13 100 gini 0.884298 0.982598
487 0.150000 13 100 entropy 0.884298 0.982598
488 0.150000 13 100 log_loss 0.900826 0.985084
489 0.150000 13 200 gini 0.896694 0.984462
490 0.150000 13 200 entropy 0.867769 0.980112
491 0.150000 13 200 log_loss 0.871901 0.980733
492 0.150000 13 300 gini 0.888430 0.983219
493 0.150000 13 300 entropy 0.880165 0.981976
494 0.150000 13 300 log_loss 0.888430 0.983219
495 0.150000 14 10 gini 0.809917 0.965817
496 0.150000 14 10 entropy 0.834711 0.971411
497 0.150000 14 10 log_loss 0.793388 0.966439
498 0.150000 14 50 gini 0.900826 0.985084
499 0.150000 14 50 entropy 0.859504 0.978869
500 0.150000 14 50 log_loss 0.880165 0.981976
501 0.150000 14 100 gini 0.880165 0.981976
502 0.150000 14 100 entropy 0.904959 0.985705
503 0.150000 14 100 log_loss 0.876033 0.981355
504 0.150000 14 200 gini 0.900826 0.985084
505 0.150000 14 200 entropy 0.863636 0.979490
506 0.150000 14 200 log_loss 0.888430 0.983219
507 0.150000 14 300 gini 0.909091 0.986327
508 0.150000 14 300 entropy 0.896694 0.984462
509 0.150000 14 300 log_loss 0.888430 0.983219
510 0.150000 15 10 gini 0.785124 0.965817
511 0.150000 15 10 entropy 0.785124 0.963953
512 0.150000 15 10 log_loss 0.814050 0.969546
513 0.150000 15 50 gini 0.896694 0.984462
514 0.150000 15 50 entropy 0.896694 0.984462
515 0.150000 15 50 log_loss 0.851240 0.977626
516 0.150000 15 100 gini 0.871901 0.980733
517 0.150000 15 100 entropy 0.892562 0.983841
518 0.150000 15 100 log_loss 0.884298 0.982598
519 0.150000 15 200 gini 0.913223 0.986948
520 0.150000 15 200 entropy 0.880165 0.981976
521 0.150000 15 200 log_loss 0.896694 0.984462
522 0.150000 15 300 gini 0.909091 0.986327
523 0.150000 15 300 entropy 0.871901 0.980733
524 0.150000 15 300 log_loss 0.884298 0.982598
525 0.150000 16 10 gini 0.776860 0.962088
526 0.150000 16 10 entropy 0.805785 0.968925
527 0.150000 16 10 log_loss 0.814050 0.966439
528 0.150000 16 50 gini 0.876033 0.981355
529 0.150000 16 50 entropy 0.884298 0.982598
530 0.150000 16 50 log_loss 0.896694 0.984462
531 0.150000 16 100 gini 0.900826 0.985084
532 0.150000 16 100 entropy 0.904959 0.985705
533 0.150000 16 100 log_loss 0.871901 0.980733
534 0.150000 16 200 gini 0.904959 0.985705
535 0.150000 16 200 entropy 0.888430 0.983219
536 0.150000 16 200 log_loss 0.892562 0.983841
537 0.150000 16 300 gini 0.896694 0.984462
538 0.150000 16 300 entropy 0.900826 0.985084
539 0.150000 16 300 log_loss 0.884298 0.982598
540 0.150000 17 10 gini 0.826446 0.969546
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542 0.150000 17 10 log_loss 0.847107 0.973275
543 0.150000 17 50 gini 0.871901 0.980733
544 0.150000 17 50 entropy 0.847107 0.977004
545 0.150000 17 50 log_loss 0.900826 0.985084
546 0.150000 17 100 gini 0.896694 0.984462
547 0.150000 17 100 entropy 0.888430 0.983219
548 0.150000 17 100 log_loss 0.888430 0.983219
549 0.150000 17 200 gini 0.904959 0.985705
550 0.150000 17 200 entropy 0.888430 0.983219
551 0.150000 17 200 log_loss 0.888430 0.983219
552 0.150000 17 300 gini 0.904959 0.985705
553 0.150000 17 300 entropy 0.880165 0.981976
554 0.150000 17 300 log_loss 0.880165 0.981976
555 0.150000 18 10 gini 0.855372 0.976383
556 0.150000 18 10 entropy 0.809917 0.968925
557 0.150000 18 10 log_loss 0.830579 0.972654
558 0.150000 18 50 gini 0.871901 0.980733
559 0.150000 18 50 entropy 0.859504 0.978869
560 0.150000 18 50 log_loss 0.880165 0.981976
561 0.150000 18 100 gini 0.880165 0.981976
562 0.150000 18 100 entropy 0.888430 0.983219
563 0.150000 18 100 log_loss 0.851240 0.977626
564 0.150000 18 200 gini 0.896694 0.984462
565 0.150000 18 200 entropy 0.847107 0.977004
566 0.150000 18 200 log_loss 0.892562 0.983841
567 0.150000 18 300 gini 0.896694 0.984462
568 0.150000 18 300 entropy 0.884298 0.982598
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570 0.150000 19 10 gini 0.822314 0.970789
571 0.150000 19 10 entropy 0.805785 0.967682
572 0.150000 19 10 log_loss 0.818182 0.968925
573 0.150000 19 50 gini 0.876033 0.981355
574 0.150000 19 50 entropy 0.896694 0.984462
575 0.150000 19 50 log_loss 0.900826 0.985084
576 0.150000 19 100 gini 0.909091 0.986327
577 0.150000 19 100 entropy 0.884298 0.982598
578 0.150000 19 100 log_loss 0.888430 0.983219
579 0.150000 19 200 gini 0.896694 0.984462
580 0.150000 19 200 entropy 0.896694 0.984462
581 0.150000 19 200 log_loss 0.884298 0.982598
582 0.150000 19 300 gini 0.892562 0.983841
583 0.150000 19 300 entropy 0.892562 0.983841
584 0.150000 19 300 log_loss 0.880165 0.981976
585 0.150000 20 10 gini 0.805785 0.967682
586 0.150000 20 10 entropy 0.838843 0.972654
587 0.150000 20 10 log_loss 0.780992 0.963331
588 0.150000 20 50 gini 0.888430 0.983219
589 0.150000 20 50 entropy 0.888430 0.983219
590 0.150000 20 50 log_loss 0.892562 0.983841
591 0.150000 20 100 gini 0.900826 0.985084
592 0.150000 20 100 entropy 0.896694 0.984462
593 0.150000 20 100 log_loss 0.867769 0.980112
594 0.150000 20 200 gini 0.909091 0.986327
595 0.150000 20 200 entropy 0.884298 0.982598
596 0.150000 20 200 log_loss 0.900826 0.985084
597 0.150000 20 300 gini 0.909091 0.986327
598 0.150000 20 300 entropy 0.892562 0.983841
599 0.150000 20 300 log_loss 0.896694 0.984462
600 0.200000 1 10 gini 0.506211 0.503418
601 0.200000 1 10 entropy 0.484472 0.513984
602 0.200000 1 10 log_loss 0.586957 0.568676
603 0.200000 1 50 gini 0.695652 0.693599
604 0.200000 1 50 entropy 0.580745 0.568055
605 0.200000 1 50 log_loss 0.593168 0.568055
606 0.200000 1 100 gini 0.717391 0.726538
607 0.200000 1 100 entropy 0.602484 0.592293
608 0.200000 1 100 log_loss 0.605590 0.586078
609 0.200000 1 200 gini 0.686335 0.699814
610 0.200000 1 200 entropy 0.605590 0.591050
611 0.200000 1 200 log_loss 0.614907 0.591672
612 0.200000 1 300 gini 0.701863 0.709758
613 0.200000 1 300 entropy 0.611801 0.596022
614 0.200000 1 300 log_loss 0.599379 0.582349
615 0.200000 2 10 gini 0.602484 0.585457
616 0.200000 2 10 entropy 0.645963 0.686762
617 0.200000 2 10 log_loss 0.562112 0.545681
618 0.200000 2 50 gini 0.798137 0.800497
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620 0.200000 2 50 log_loss 0.742236 0.738968
621 0.200000 2 100 gini 0.798137 0.810441
622 0.200000 2 100 entropy 0.736025 0.742076
623 0.200000 2 100 log_loss 0.807453 0.789310
624 0.200000 2 200 gini 0.788820 0.806712
625 0.200000 2 200 entropy 0.751553 0.749534
626 0.200000 2 200 log_loss 0.788820 0.784960
627 0.200000 2 300 gini 0.782609 0.798011
628 0.200000 2 300 entropy 0.785714 0.784338
629 0.200000 2 300 log_loss 0.791925 0.801119
630 0.200000 3 10 gini 0.736025 0.785581
631 0.200000 3 10 entropy 0.714286 0.718459
632 0.200000 3 10 log_loss 0.661491 0.675575
633 0.200000 3 50 gini 0.795031 0.829086
634 0.200000 3 50 entropy 0.767081 0.781852
635 0.200000 3 50 log_loss 0.819876 0.836544
636 0.200000 3 100 gini 0.798137 0.832815
637 0.200000 3 100 entropy 0.822981 0.836544
638 0.200000 3 100 log_loss 0.804348 0.830951
639 0.200000 3 200 gini 0.813665 0.846489
640 0.200000 3 200 entropy 0.822981 0.849596
641 0.200000 3 200 log_loss 0.826087 0.844002
642 0.200000 3 300 gini 0.826087 0.848353
643 0.200000 3 300 entropy 0.807453 0.842138
644 0.200000 3 300 log_loss 0.822981 0.845245
645 0.200000 4 10 gini 0.726708 0.780609
646 0.200000 4 10 entropy 0.776398 0.821628
647 0.200000 4 10 log_loss 0.785714 0.812927
648 0.200000 4 50 gini 0.838509 0.869484
649 0.200000 4 50 entropy 0.822981 0.862648
650 0.200000 4 50 log_loss 0.813665 0.870727
651 0.200000 4 100 gini 0.832298 0.884400
652 0.200000 4 100 entropy 0.829193 0.861405
653 0.200000 4 100 log_loss 0.819876 0.863269
654 0.200000 4 200 gini 0.832298 0.878185
655 0.200000 4 200 entropy 0.847826 0.878807
656 0.200000 4 200 log_loss 0.838509 0.875078
657 0.200000 4 300 gini 0.816770 0.871970
658 0.200000 4 300 entropy 0.832298 0.877564
659 0.200000 4 300 log_loss 0.835404 0.870727
660 0.200000 5 10 gini 0.782609 0.845867
661 0.200000 5 10 entropy 0.739130 0.841516
662 0.200000 5 10 log_loss 0.757764 0.836544
663 0.200000 5 50 gini 0.832298 0.901802
664 0.200000 5 50 entropy 0.826087 0.900559
665 0.200000 5 50 log_loss 0.813665 0.885643
666 0.200000 5 100 gini 0.875776 0.916718
667 0.200000 5 100 entropy 0.832298 0.896209
668 0.200000 5 100 log_loss 0.838509 0.904288
669 0.200000 5 200 gini 0.857143 0.911125
670 0.200000 5 200 entropy 0.835404 0.899938
671 0.200000 5 200 log_loss 0.841615 0.897452
672 0.200000 5 300 gini 0.835404 0.914232
673 0.200000 5 300 entropy 0.829193 0.896830
674 0.200000 5 300 log_loss 0.832298 0.894966
675 0.200000 6 10 gini 0.757764 0.865755
676 0.200000 6 10 entropy 0.791925 0.881293
677 0.200000 6 10 log_loss 0.804348 0.894966
678 0.200000 6 50 gini 0.844720 0.925420
679 0.200000 6 50 entropy 0.847826 0.931013
680 0.200000 6 50 log_loss 0.829193 0.926663
681 0.200000 6 100 gini 0.854037 0.937228
682 0.200000 6 100 entropy 0.854037 0.932256
683 0.200000 6 100 log_loss 0.850932 0.934121
684 0.200000 6 200 gini 0.860248 0.940957
685 0.200000 6 200 entropy 0.841615 0.929770
686 0.200000 6 200 log_loss 0.844720 0.938471
687 0.200000 6 300 gini 0.847826 0.936607
688 0.200000 6 300 entropy 0.847826 0.934121
689 0.200000 6 300 log_loss 0.841615 0.931635
690 0.200000 7 10 gini 0.826087 0.918583
691 0.200000 7 10 entropy 0.785714 0.904910
692 0.200000 7 10 log_loss 0.860248 0.934742
693 0.200000 7 50 gini 0.860248 0.944065
694 0.200000 7 50 entropy 0.881988 0.958359
695 0.200000 7 50 log_loss 0.844720 0.954009
696 0.200000 7 100 gini 0.860248 0.956495
697 0.200000 7 100 entropy 0.869565 0.960845
698 0.200000 7 100 log_loss 0.854037 0.954630
699 0.200000 7 200 gini 0.863354 0.959602
700 0.200000 7 200 entropy 0.857143 0.956495
701 0.200000 7 200 log_loss 0.860248 0.957116
702 0.200000 7 300 gini 0.860248 0.954009
703 0.200000 7 300 entropy 0.872671 0.963953
704 0.200000 7 300 log_loss 0.850932 0.956495
705 0.200000 8 10 gini 0.813665 0.933499
706 0.200000 8 10 entropy 0.816770 0.937850
707 0.200000 8 10 log_loss 0.813665 0.941579
708 0.200000 8 50 gini 0.885093 0.970789
709 0.200000 8 50 entropy 0.875776 0.969546
710 0.200000 8 50 log_loss 0.844720 0.962088
711 0.200000 8 100 gini 0.875776 0.968303
712 0.200000 8 100 entropy 0.863354 0.969546
713 0.200000 8 100 log_loss 0.869565 0.969546
714 0.200000 8 200 gini 0.888199 0.970789
715 0.200000 8 200 entropy 0.860248 0.968303
716 0.200000 8 200 log_loss 0.878882 0.973275
717 0.200000 8 300 gini 0.872671 0.968303
718 0.200000 8 300 entropy 0.869565 0.968925
719 0.200000 8 300 log_loss 0.878882 0.972654
720 0.200000 9 10 gini 0.798137 0.940336
721 0.200000 9 10 entropy 0.826087 0.953387
722 0.200000 9 10 log_loss 0.826087 0.949658
723 0.200000 9 50 gini 0.869565 0.972654
724 0.200000 9 50 entropy 0.860248 0.972032
725 0.200000 9 50 log_loss 0.881988 0.975140
726 0.200000 9 100 gini 0.866460 0.970789
727 0.200000 9 100 entropy 0.869565 0.973275
728 0.200000 9 100 log_loss 0.869565 0.973275
729 0.200000 9 200 gini 0.875776 0.973897
730 0.200000 9 200 entropy 0.866460 0.972654
731 0.200000 9 200 log_loss 0.872671 0.974518
732 0.200000 9 300 gini 0.878882 0.973897
733 0.200000 9 300 entropy 0.872671 0.974518
734 0.200000 9 300 log_loss 0.872671 0.973897
735 0.200000 10 10 gini 0.838509 0.953387
736 0.200000 10 10 entropy 0.819876 0.958981
737 0.200000 10 10 log_loss 0.807453 0.955252
738 0.200000 10 50 gini 0.878882 0.973897
739 0.200000 10 50 entropy 0.863354 0.972032
740 0.200000 10 50 log_loss 0.878882 0.975140
741 0.200000 10 100 gini 0.888199 0.977626
742 0.200000 10 100 entropy 0.885093 0.977004
743 0.200000 10 100 log_loss 0.881988 0.976383
744 0.200000 10 200 gini 0.875776 0.974518
745 0.200000 10 200 entropy 0.872671 0.974518
746 0.200000 10 200 log_loss 0.872671 0.974518
747 0.200000 10 300 gini 0.878882 0.975761
748 0.200000 10 300 entropy 0.875776 0.975140
749 0.200000 10 300 log_loss 0.891304 0.978247
750 0.200000 11 10 gini 0.804348 0.954009
751 0.200000 11 10 entropy 0.835404 0.961467
752 0.200000 11 10 log_loss 0.819876 0.961467
753 0.200000 11 50 gini 0.844720 0.967682
754 0.200000 11 50 entropy 0.875776 0.974518
755 0.200000 11 50 log_loss 0.863354 0.972654
756 0.200000 11 100 gini 0.869565 0.973897
757 0.200000 11 100 entropy 0.888199 0.977626
758 0.200000 11 100 log_loss 0.872671 0.974518
759 0.200000 11 200 gini 0.872671 0.974518
760 0.200000 11 200 entropy 0.894410 0.978869
761 0.200000 11 200 log_loss 0.881988 0.976383
762 0.200000 11 300 gini 0.881988 0.976383
763 0.200000 11 300 entropy 0.878882 0.975761
764 0.200000 11 300 log_loss 0.897516 0.979490
765 0.200000 12 10 gini 0.788820 0.954630
766 0.200000 12 10 entropy 0.785714 0.954630
767 0.200000 12 10 log_loss 0.788820 0.951523
768 0.200000 12 50 gini 0.878882 0.975761
769 0.200000 12 50 entropy 0.869565 0.973897
770 0.200000 12 50 log_loss 0.863354 0.972654
771 0.200000 12 100 gini 0.888199 0.977626
772 0.200000 12 100 entropy 0.885093 0.977004
773 0.200000 12 100 log_loss 0.869565 0.973897
774 0.200000 12 200 gini 0.891304 0.978247
775 0.200000 12 200 entropy 0.891304 0.978247
776 0.200000 12 200 log_loss 0.878882 0.975761
777 0.200000 12 300 gini 0.900621 0.980112
778 0.200000 12 300 entropy 0.888199 0.977626
779 0.200000 12 300 log_loss 0.888199 0.977626
780 0.200000 13 10 gini 0.801242 0.954630
781 0.200000 13 10 entropy 0.816770 0.961467
782 0.200000 13 10 log_loss 0.844720 0.965817
783 0.200000 13 50 gini 0.881988 0.976383
784 0.200000 13 50 entropy 0.875776 0.975140
785 0.200000 13 50 log_loss 0.860248 0.972032
786 0.200000 13 100 gini 0.881988 0.976383
787 0.200000 13 100 entropy 0.866460 0.973275
788 0.200000 13 100 log_loss 0.878882 0.975761
789 0.200000 13 200 gini 0.888199 0.977626
790 0.200000 13 200 entropy 0.885093 0.977004
791 0.200000 13 200 log_loss 0.881988 0.976383
792 0.200000 13 300 gini 0.881988 0.976383
793 0.200000 13 300 entropy 0.888199 0.977626
794 0.200000 13 300 log_loss 0.872671 0.974518
795 0.200000 14 10 gini 0.751553 0.947794
796 0.200000 14 10 entropy 0.844720 0.962088
797 0.200000 14 10 log_loss 0.776398 0.952766
798 0.200000 14 50 gini 0.854037 0.970789
799 0.200000 14 50 entropy 0.881988 0.976383
800 0.200000 14 50 log_loss 0.844720 0.968925
801 0.200000 14 100 gini 0.875776 0.975140
802 0.200000 14 100 entropy 0.894410 0.978869
803 0.200000 14 100 log_loss 0.875776 0.975140
804 0.200000 14 200 gini 0.885093 0.977004
805 0.200000 14 200 entropy 0.881988 0.976383
806 0.200000 14 200 log_loss 0.878882 0.975761
807 0.200000 14 300 gini 0.888199 0.977626
808 0.200000 14 300 entropy 0.891304 0.978247
809 0.200000 14 300 log_loss 0.885093 0.977004
810 0.200000 15 10 gini 0.804348 0.955252
811 0.200000 15 10 entropy 0.795031 0.956495
812 0.200000 15 10 log_loss 0.779503 0.952766
813 0.200000 15 50 gini 0.875776 0.975140
814 0.200000 15 50 entropy 0.854037 0.970789
815 0.200000 15 50 log_loss 0.844720 0.968925
816 0.200000 15 100 gini 0.885093 0.977004
817 0.200000 15 100 entropy 0.888199 0.977626
818 0.200000 15 100 log_loss 0.875776 0.975140
819 0.200000 15 200 gini 0.881988 0.976383
820 0.200000 15 200 entropy 0.878882 0.975761
821 0.200000 15 200 log_loss 0.878882 0.975761
822 0.200000 15 300 gini 0.894410 0.978869
823 0.200000 15 300 entropy 0.891304 0.978247
824 0.200000 15 300 log_loss 0.888199 0.977626
825 0.200000 16 10 gini 0.847826 0.964574
826 0.200000 16 10 entropy 0.807453 0.956495
827 0.200000 16 10 log_loss 0.788820 0.953387
828 0.200000 16 50 gini 0.857143 0.971411
829 0.200000 16 50 entropy 0.869565 0.973897
830 0.200000 16 50 log_loss 0.891304 0.978247
831 0.200000 16 100 gini 0.894410 0.978869
832 0.200000 16 100 entropy 0.875776 0.975140
833 0.200000 16 100 log_loss 0.869565 0.973897
834 0.200000 16 200 gini 0.888199 0.977626
835 0.200000 16 200 entropy 0.885093 0.977004
836 0.200000 16 200 log_loss 0.875776 0.975140
837 0.200000 16 300 gini 0.885093 0.977004
838 0.200000 16 300 entropy 0.885093 0.977004
839 0.200000 16 300 log_loss 0.878882 0.975761
840 0.200000 17 10 gini 0.807453 0.960224
841 0.200000 17 10 entropy 0.826087 0.961467
842 0.200000 17 10 log_loss 0.810559 0.955873
843 0.200000 17 50 gini 0.863354 0.972654
844 0.200000 17 50 entropy 0.881988 0.976383
845 0.200000 17 50 log_loss 0.863354 0.972654
846 0.200000 17 100 gini 0.897516 0.979490
847 0.200000 17 100 entropy 0.900621 0.980112
848 0.200000 17 100 log_loss 0.860248 0.972032
849 0.200000 17 200 gini 0.878882 0.975761
850 0.200000 17 200 entropy 0.897516 0.979490
851 0.200000 17 200 log_loss 0.872671 0.974518
852 0.200000 17 300 gini 0.888199 0.977626
853 0.200000 17 300 entropy 0.885093 0.977004
854 0.200000 17 300 log_loss 0.881988 0.976383
855 0.200000 18 10 gini 0.739130 0.941579
856 0.200000 18 10 entropy 0.816770 0.961467
857 0.200000 18 10 log_loss 0.785714 0.952766
858 0.200000 18 50 gini 0.875776 0.975140
859 0.200000 18 50 entropy 0.863354 0.972654
860 0.200000 18 50 log_loss 0.872671 0.974518
861 0.200000 18 100 gini 0.885093 0.977004
862 0.200000 18 100 entropy 0.881988 0.976383
863 0.200000 18 100 log_loss 0.872671 0.974518
864 0.200000 18 200 gini 0.891304 0.978247
865 0.200000 18 200 entropy 0.866460 0.973275
866 0.200000 18 200 log_loss 0.885093 0.977004
867 0.200000 18 300 gini 0.881988 0.976383
868 0.200000 18 300 entropy 0.885093 0.977004
869 0.200000 18 300 log_loss 0.891304 0.978247
870 0.200000 19 10 gini 0.804348 0.960224
871 0.200000 19 10 entropy 0.819876 0.960845
872 0.200000 19 10 log_loss 0.841615 0.962710
873 0.200000 19 50 gini 0.857143 0.971411
874 0.200000 19 50 entropy 0.860248 0.972032
875 0.200000 19 50 log_loss 0.869565 0.973897
876 0.200000 19 100 gini 0.878882 0.975761
877 0.200000 19 100 entropy 0.866460 0.973275
878 0.200000 19 100 log_loss 0.894410 0.978869
879 0.200000 19 200 gini 0.888199 0.977626
880 0.200000 19 200 entropy 0.888199 0.977626
881 0.200000 19 200 log_loss 0.878882 0.975761
882 0.200000 19 300 gini 0.885093 0.977004
883 0.200000 19 300 entropy 0.888199 0.977626
884 0.200000 19 300 log_loss 0.881988 0.976383
885 0.200000 20 10 gini 0.810559 0.958981
886 0.200000 20 10 entropy 0.826087 0.959602
887 0.200000 20 10 log_loss 0.810559 0.957738
888 0.200000 20 50 gini 0.878882 0.975761
889 0.200000 20 50 entropy 0.869565 0.973897
890 0.200000 20 50 log_loss 0.885093 0.977004
891 0.200000 20 100 gini 0.872671 0.974518
892 0.200000 20 100 entropy 0.888199 0.977626
893 0.200000 20 100 log_loss 0.869565 0.973897
894 0.200000 20 200 gini 0.885093 0.977004
895 0.200000 20 200 entropy 0.885093 0.977004
896 0.200000 20 200 log_loss 0.872671 0.974518
897 0.200000 20 300 gini 0.878882 0.975761
898 0.200000 20 300 entropy 0.878882 0.975761
899 0.200000 20 300 log_loss 0.885093 0.977004
900 0.250000 1 10 gini 0.523573 0.539466
901 0.250000 1 10 entropy 0.543424 0.527035
902 0.250000 1 10 log_loss 0.553350 0.554382
903 0.250000 1 50 gini 0.660050 0.685519
904 0.250000 1 50 entropy 0.550868 0.574891
905 0.250000 1 50 log_loss 0.585608 0.582349
906 0.250000 1 100 gini 0.657568 0.691734
907 0.250000 1 100 entropy 0.570720 0.567433
908 0.250000 1 100 log_loss 0.570720 0.589186
909 0.250000 1 200 gini 0.662531 0.697328
910 0.250000 1 200 entropy 0.590571 0.580485
911 0.250000 1 200 log_loss 0.565757 0.569298
912 0.250000 1 300 gini 0.669975 0.701057
913 0.250000 1 300 entropy 0.583127 0.587321
914 0.250000 1 300 log_loss 0.593052 0.600994
915 0.250000 2 10 gini 0.754342 0.765693
916 0.250000 2 10 entropy 0.682382 0.700435
917 0.250000 2 10 log_loss 0.660050 0.704164
918 0.250000 2 50 gini 0.722084 0.752641
919 0.250000 2 50 entropy 0.632754 0.645121
920 0.250000 2 50 log_loss 0.689826 0.717216
921 0.250000 2 100 gini 0.774194 0.799254
922 0.250000 2 100 entropy 0.724566 0.742076
923 0.250000 2 100 log_loss 0.774194 0.789932
924 0.250000 2 200 gini 0.764268 0.791796
925 0.250000 2 200 entropy 0.744417 0.766314
926 0.250000 2 200 log_loss 0.727047 0.753263
927 0.250000 2 300 gini 0.761787 0.793039
928 0.250000 2 300 entropy 0.769231 0.782474
929 0.250000 2 300 log_loss 0.704715 0.720945
930 0.250000 3 10 gini 0.518610 0.580485
931 0.250000 3 10 entropy 0.734491 0.747048
932 0.250000 3 10 log_loss 0.642680 0.677439
933 0.250000 3 50 gini 0.781638 0.817899
934 0.250000 3 50 entropy 0.729529 0.782474
935 0.250000 3 50 log_loss 0.796526 0.836544
936 0.250000 3 100 gini 0.808933 0.839030
937 0.250000 3 100 entropy 0.781638 0.817278
938 0.250000 3 100 log_loss 0.806452 0.839652
939 0.250000 3 200 gini 0.794045 0.833437
940 0.250000 3 200 entropy 0.801489 0.835301
941 0.250000 3 200 log_loss 0.826303 0.841516
942 0.250000 3 300 gini 0.808933 0.845867
943 0.250000 3 300 entropy 0.808933 0.836544
944 0.250000 3 300 log_loss 0.813896 0.845867
945 0.250000 4 10 gini 0.771712 0.803605
946 0.250000 4 10 entropy 0.674938 0.747669
947 0.250000 4 10 log_loss 0.756824 0.810441
948 0.250000 4 50 gini 0.776675 0.847110
949 0.250000 4 50 entropy 0.806452 0.858297
950 0.250000 4 50 log_loss 0.799007 0.856433
951 0.250000 4 100 gini 0.816377 0.875078
952 0.250000 4 100 entropy 0.836228 0.875078
953 0.250000 4 100 log_loss 0.808933 0.867620
954 0.250000 4 200 gini 0.828784 0.877564
955 0.250000 4 200 entropy 0.823821 0.869484
956 0.250000 4 200 log_loss 0.813896 0.868863
957 0.250000 4 300 gini 0.828784 0.871349
958 0.250000 4 300 entropy 0.826303 0.875699
959 0.250000 4 300 log_loss 0.826303 0.877564
960 0.250000 5 10 gini 0.744417 0.837166
961 0.250000 5 10 entropy 0.769231 0.845245
962 0.250000 5 10 log_loss 0.789082 0.844624
963 0.250000 5 50 gini 0.836228 0.904910
964 0.250000 5 50 entropy 0.811414 0.891858
965 0.250000 5 50 log_loss 0.833747 0.893101
966 0.250000 5 100 gini 0.843672 0.901181
967 0.250000 5 100 entropy 0.826303 0.891237
968 0.250000 5 100 log_loss 0.823821 0.896830
969 0.250000 5 200 gini 0.833747 0.902424
970 0.250000 5 200 entropy 0.833747 0.898695
971 0.250000 5 200 log_loss 0.838710 0.894344
972 0.250000 5 300 gini 0.831266 0.904288
973 0.250000 5 300 entropy 0.838710 0.898073
974 0.250000 5 300 log_loss 0.846154 0.901802
975 0.250000 6 10 gini 0.769231 0.870727
976 0.250000 6 10 entropy 0.781638 0.898695
977 0.250000 6 10 log_loss 0.794045 0.881914
978 0.250000 6 50 gini 0.836228 0.920447
979 0.250000 6 50 entropy 0.841191 0.923555
980 0.250000 6 50 log_loss 0.848635 0.926663
981 0.250000 6 100 gini 0.833747 0.923555
982 0.250000 6 100 entropy 0.851117 0.927284
983 0.250000 6 100 log_loss 0.841191 0.927906
984 0.250000 6 200 gini 0.851117 0.932878
985 0.250000 6 200 entropy 0.843672 0.926663
986 0.250000 6 200 log_loss 0.858561 0.929770
987 0.250000 6 300 gini 0.863524 0.936607
988 0.250000 6 300 entropy 0.848635 0.930392
989 0.250000 6 300 log_loss 0.853598 0.931013
990 0.250000 7 10 gini 0.781638 0.911746
991 0.250000 7 10 entropy 0.826303 0.925420
992 0.250000 7 10 log_loss 0.741935 0.877564
993 0.250000 7 50 gini 0.851117 0.940336
994 0.250000 7 50 entropy 0.833747 0.940957
995 0.250000 7 50 log_loss 0.858561 0.951523
996 0.250000 7 100 gini 0.861042 0.950280
997 0.250000 7 100 entropy 0.841191 0.942822
998 0.250000 7 100 log_loss 0.873449 0.952766
999 0.250000 7 200 gini 0.853598 0.949658
1000 0.250000 7 200 entropy 0.858561 0.954630
1001 0.250000 7 200 log_loss 0.866005 0.955873
1002 0.250000 7 300 gini 0.863524 0.950280
1003 0.250000 7 300 entropy 0.856079 0.952144
1004 0.250000 7 300 log_loss 0.863524 0.954630
1005 0.250000 8 10 gini 0.803970 0.922312
1006 0.250000 8 10 entropy 0.796526 0.924798
1007 0.250000 8 10 log_loss 0.843672 0.946551
1008 0.250000 8 50 gini 0.843672 0.953387
1009 0.250000 8 50 entropy 0.863524 0.960224
1010 0.250000 8 50 log_loss 0.836228 0.949658
1011 0.250000 8 100 gini 0.866005 0.960845
1012 0.250000 8 100 entropy 0.863524 0.961467
1013 0.250000 8 100 log_loss 0.880893 0.966439
1014 0.250000 8 200 gini 0.858561 0.957738
1015 0.250000 8 200 entropy 0.870968 0.966439
1016 0.250000 8 200 log_loss 0.870968 0.965817
1017 0.250000 8 300 gini 0.851117 0.958359
1018 0.250000 8 300 entropy 0.856079 0.961467
1019 0.250000 8 300 log_loss 0.873449 0.964574
1020 0.250000 9 10 gini 0.769231 0.923555
1021 0.250000 9 10 entropy 0.789082 0.937228
1022 0.250000 9 10 log_loss 0.784119 0.926663
1023 0.250000 9 50 gini 0.828784 0.954630
1024 0.250000 9 50 entropy 0.858561 0.961467
1025 0.250000 9 50 log_loss 0.851117 0.961467
1026 0.250000 9 100 gini 0.856079 0.962088
1027 0.250000 9 100 entropy 0.866005 0.966439
1028 0.250000 9 100 log_loss 0.848635 0.962088
1029 0.250000 9 200 gini 0.875931 0.967682
1030 0.250000 9 200 entropy 0.870968 0.967060
1031 0.250000 9 200 log_loss 0.873449 0.967682
1032 0.250000 9 300 gini 0.873449 0.966439
1033 0.250000 9 300 entropy 0.875931 0.968925
1034 0.250000 9 300 log_loss 0.875931 0.968303
1035 0.250000 10 10 gini 0.796526 0.939714
1036 0.250000 10 10 entropy 0.803970 0.942822
1037 0.250000 10 10 log_loss 0.789082 0.943443
1038 0.250000 10 50 gini 0.848635 0.960845
1039 0.250000 10 50 entropy 0.866005 0.966439
1040 0.250000 10 50 log_loss 0.863524 0.965817
1041 0.250000 10 100 gini 0.873449 0.967682
1042 0.250000 10 100 entropy 0.861042 0.965196
1043 0.250000 10 100 log_loss 0.873449 0.968303
1044 0.250000 10 200 gini 0.878412 0.968925
1045 0.250000 10 200 entropy 0.880893 0.970168
1046 0.250000 10 200 log_loss 0.873449 0.968303
1047 0.250000 10 300 gini 0.866005 0.965817
1048 0.250000 10 300 entropy 0.880893 0.970168
1049 0.250000 10 300 log_loss 0.863524 0.965817
1050 0.250000 11 10 gini 0.789082 0.939093
1051 0.250000 11 10 entropy 0.761787 0.934742
1052 0.250000 11 10 log_loss 0.776675 0.937228
1053 0.250000 11 50 gini 0.866005 0.966439
1054 0.250000 11 50 entropy 0.880893 0.970168
1055 0.250000 11 50 log_loss 0.863524 0.965817
1056 0.250000 11 100 gini 0.856079 0.963331
1057 0.250000 11 100 entropy 0.866005 0.966439
1058 0.250000 11 100 log_loss 0.880893 0.970168
1059 0.250000 11 200 gini 0.878412 0.969546
1060 0.250000 11 200 entropy 0.895782 0.973897
1061 0.250000 11 200 log_loss 0.880893 0.970168
1062 0.250000 11 300 gini 0.875931 0.968925
1063 0.250000 11 300 entropy 0.868486 0.967060
1064 0.250000 11 300 log_loss 0.858561 0.964574
1065 0.250000 12 10 gini 0.796526 0.941579
1066 0.250000 12 10 entropy 0.818859 0.952144
1067 0.250000 12 10 log_loss 0.816377 0.949658
1068 0.250000 12 50 gini 0.868486 0.967060
1069 0.250000 12 50 entropy 0.870968 0.967682
1070 0.250000 12 50 log_loss 0.858561 0.964574
1071 0.250000 12 100 gini 0.866005 0.966439
1072 0.250000 12 100 entropy 0.875931 0.968925
1073 0.250000 12 100 log_loss 0.895782 0.973897
1074 0.250000 12 200 gini 0.873449 0.968303
1075 0.250000 12 200 entropy 0.883375 0.970789
1076 0.250000 12 200 log_loss 0.885856 0.971411
1077 0.250000 12 300 gini 0.885856 0.971411
1078 0.250000 12 300 entropy 0.870968 0.967682
1079 0.250000 12 300 log_loss 0.870968 0.967682
1080 0.250000 13 10 gini 0.833747 0.952766
1081 0.250000 13 10 entropy 0.823821 0.952144
1082 0.250000 13 10 log_loss 0.774194 0.935985
1083 0.250000 13 50 gini 0.861042 0.965196
1084 0.250000 13 50 entropy 0.863524 0.965196
1085 0.250000 13 50 log_loss 0.875931 0.968925
1086 0.250000 13 100 gini 0.883375 0.970789
1087 0.250000 13 100 entropy 0.873449 0.968303
1088 0.250000 13 100 log_loss 0.888337 0.972032
1089 0.250000 13 200 gini 0.870968 0.967682
1090 0.250000 13 200 entropy 0.875931 0.968925
1091 0.250000 13 200 log_loss 0.880893 0.970168
1092 0.250000 13 300 gini 0.866005 0.966439
1093 0.250000 13 300 entropy 0.873449 0.968303
1094 0.250000 13 300 log_loss 0.873449 0.968303
1095 0.250000 14 10 gini 0.801489 0.945929
1096 0.250000 14 10 entropy 0.861042 0.961467
1097 0.250000 14 10 log_loss 0.779156 0.942200
1098 0.250000 14 50 gini 0.851117 0.962710
1099 0.250000 14 50 entropy 0.863524 0.965817
1100 0.250000 14 50 log_loss 0.846154 0.961467
1101 0.250000 14 100 gini 0.880893 0.970168
1102 0.250000 14 100 entropy 0.868486 0.967060
1103 0.250000 14 100 log_loss 0.880893 0.970168
1104 0.250000 14 200 gini 0.866005 0.966439
1105 0.250000 14 200 entropy 0.890819 0.972654
1106 0.250000 14 200 log_loss 0.873449 0.968303
1107 0.250000 14 300 gini 0.870968 0.967682
1108 0.250000 14 300 entropy 0.878412 0.969546
1109 0.250000 14 300 log_loss 0.883375 0.970789
1110 0.250000 15 10 gini 0.794045 0.943443
1111 0.250000 15 10 entropy 0.818859 0.950901
1112 0.250000 15 10 log_loss 0.789082 0.944065
1113 0.250000 15 50 gini 0.841191 0.960224
1114 0.250000 15 50 entropy 0.878412 0.969546
1115 0.250000 15 50 log_loss 0.866005 0.966439
1116 0.250000 15 100 gini 0.878412 0.969546
1117 0.250000 15 100 entropy 0.848635 0.962088
1118 0.250000 15 100 log_loss 0.866005 0.966439
1119 0.250000 15 200 gini 0.870968 0.967682
1120 0.250000 15 200 entropy 0.873449 0.968303
1121 0.250000 15 200 log_loss 0.878412 0.969546
1122 0.250000 15 300 gini 0.873449 0.968303
1123 0.250000 15 300 entropy 0.873449 0.968303
1124 0.250000 15 300 log_loss 0.883375 0.970789
1125 0.250000 16 10 gini 0.794045 0.944065
1126 0.250000 16 10 entropy 0.806452 0.947172
1127 0.250000 16 10 log_loss 0.826303 0.954009
1128 0.250000 16 50 gini 0.883375 0.970789
1129 0.250000 16 50 entropy 0.875931 0.968925
1130 0.250000 16 50 log_loss 0.853598 0.963331
1131 0.250000 16 100 gini 0.873449 0.968303
1132 0.250000 16 100 entropy 0.883375 0.970789
1133 0.250000 16 100 log_loss 0.880893 0.970168
1134 0.250000 16 200 gini 0.883375 0.970789
1135 0.250000 16 200 entropy 0.883375 0.970789
1136 0.250000 16 200 log_loss 0.873449 0.968303
1137 0.250000 16 300 gini 0.866005 0.966439
1138 0.250000 16 300 entropy 0.878412 0.969546
1139 0.250000 16 300 log_loss 0.888337 0.972032
1140 0.250000 17 10 gini 0.801489 0.947794
1141 0.250000 17 10 entropy 0.786600 0.944065
1142 0.250000 17 10 log_loss 0.803970 0.947172
1143 0.250000 17 50 gini 0.836228 0.958981
1144 0.250000 17 50 entropy 0.873449 0.968303
1145 0.250000 17 50 log_loss 0.898263 0.974518
1146 0.250000 17 100 gini 0.856079 0.963953
1147 0.250000 17 100 entropy 0.885856 0.971411
1148 0.250000 17 100 log_loss 0.878412 0.969546
1149 0.250000 17 200 gini 0.880893 0.970168
1150 0.250000 17 200 entropy 0.883375 0.970789
1151 0.250000 17 200 log_loss 0.880893 0.970168
1152 0.250000 17 300 gini 0.868486 0.967060
1153 0.250000 17 300 entropy 0.878412 0.969546
1154 0.250000 17 300 log_loss 0.875931 0.968925
1155 0.250000 18 10 gini 0.786600 0.944065
1156 0.250000 18 10 entropy 0.833747 0.955252
1157 0.250000 18 10 log_loss 0.794045 0.946551
1158 0.250000 18 50 gini 0.866005 0.966439
1159 0.250000 18 50 entropy 0.868486 0.967060
1160 0.250000 18 50 log_loss 0.856079 0.963953
1161 0.250000 18 100 gini 0.880893 0.970168
1162 0.250000 18 100 entropy 0.868486 0.967060
1163 0.250000 18 100 log_loss 0.870968 0.967682
1164 0.250000 18 200 gini 0.880893 0.970168
1165 0.250000 18 200 entropy 0.878412 0.969546
1166 0.250000 18 200 log_loss 0.873449 0.968303
1167 0.250000 18 300 gini 0.878412 0.969546
1168 0.250000 18 300 entropy 0.883375 0.970789
1169 0.250000 18 300 log_loss 0.875931 0.968925
1170 0.250000 19 10 gini 0.756824 0.935364
1171 0.250000 19 10 entropy 0.826303 0.951523
1172 0.250000 19 10 log_loss 0.831266 0.956495
1173 0.250000 19 50 gini 0.866005 0.966439
1174 0.250000 19 50 entropy 0.863524 0.965817
1175 0.250000 19 50 log_loss 0.863524 0.965817
1176 0.250000 19 100 gini 0.853598 0.963331
1177 0.250000 19 100 entropy 0.861042 0.965196
1178 0.250000 19 100 log_loss 0.878412 0.969546
1179 0.250000 19 200 gini 0.878412 0.969546
1180 0.250000 19 200 entropy 0.868486 0.967060
1181 0.250000 19 200 log_loss 0.875931 0.968925
1182 0.250000 19 300 gini 0.878412 0.969546
1183 0.250000 19 300 entropy 0.878412 0.969546
1184 0.250000 19 300 log_loss 0.868486 0.967060
1185 0.250000 20 10 gini 0.744417 0.934742
1186 0.250000 20 10 entropy 0.813896 0.951523
1187 0.250000 20 10 log_loss 0.781638 0.941579
1188 0.250000 20 50 gini 0.868486 0.967060
1189 0.250000 20 50 entropy 0.868486 0.967060
1190 0.250000 20 50 log_loss 0.863524 0.965817
1191 0.250000 20 100 gini 0.858561 0.964574
1192 0.250000 20 100 entropy 0.868486 0.967060
1193 0.250000 20 100 log_loss 0.883375 0.970789
1194 0.250000 20 200 gini 0.866005 0.966439
1195 0.250000 20 200 entropy 0.868486 0.967060
1196 0.250000 20 200 log_loss 0.890819 0.972654
1197 0.250000 20 300 gini 0.868486 0.967060
1198 0.250000 20 300 entropy 0.878412 0.969546
1199 0.250000 20 300 log_loss 0.885856 0.971411
1200 0.300000 1 10 gini 0.525880 0.545681
1201 0.300000 1 10 entropy 0.654244 0.668738
1202 0.300000 1 10 log_loss 0.509317 0.517091
1203 0.300000 1 50 gini 0.614907 0.653201
1204 0.300000 1 50 entropy 0.573499 0.582349
1205 0.300000 1 50 log_loss 0.550725 0.553139
1206 0.300000 1 100 gini 0.679089 0.696085
1207 0.300000 1 100 entropy 0.587992 0.596644
1208 0.300000 1 100 log_loss 0.577640 0.576756
1209 0.300000 1 200 gini 0.654244 0.672467
1210 0.300000 1 200 entropy 0.573499 0.583592
1211 0.300000 1 200 log_loss 0.565217 0.559975
1212 0.300000 1 300 gini 0.706004 0.714108
1213 0.300000 1 300 entropy 0.616977 0.610317
1214 0.300000 1 300 log_loss 0.585921 0.592915
1215 0.300000 2 10 gini 0.598344 0.617154
1216 0.300000 2 10 entropy 0.544513 0.556868
1217 0.300000 2 10 log_loss 0.662526 0.678061
1218 0.300000 2 50 gini 0.737060 0.740211
1219 0.300000 2 50 entropy 0.755694 0.766936
1220 0.300000 2 50 log_loss 0.643892 0.668738
1221 0.300000 2 100 gini 0.788820 0.778745
1222 0.300000 2 100 entropy 0.751553 0.766936
1223 0.300000 2 100 log_loss 0.724638 0.735861
1224 0.300000 2 200 gini 0.792961 0.806712
1225 0.300000 2 200 entropy 0.693582 0.711001
1226 0.300000 2 200 log_loss 0.774327 0.790553
1227 0.300000 2 300 gini 0.774327 0.778123
1228 0.300000 2 300 entropy 0.743271 0.738968
1229 0.300000 2 300 log_loss 0.761905 0.761964
1230 0.300000 3 10 gini 0.710145 0.768179
1231 0.300000 3 10 entropy 0.629400 0.656308
1232 0.300000 3 10 log_loss 0.693582 0.717216
1233 0.300000 3 50 gini 0.815735 0.832194
1234 0.300000 3 50 entropy 0.811594 0.817899
1235 0.300000 3 50 log_loss 0.768116 0.800497
1236 0.300000 3 100 gini 0.790890 0.824736
1237 0.300000 3 100 entropy 0.811594 0.830329
1238 0.300000 3 100 log_loss 0.815735 0.840273
1239 0.300000 3 200 gini 0.824017 0.842759
1240 0.300000 3 200 entropy 0.813665 0.839652
1241 0.300000 3 200 log_loss 0.828157 0.844002
1242 0.300000 3 300 gini 0.826087 0.840895
1243 0.300000 3 300 entropy 0.805383 0.825979
1244 0.300000 3 300 log_loss 0.815735 0.838409
1245 0.300000 4 10 gini 0.734990 0.766314
1246 0.300000 4 10 entropy 0.759834 0.801740
1247 0.300000 4 10 log_loss 0.639752 0.721566
1248 0.300000 4 50 gini 0.828157 0.860783
1249 0.300000 4 50 entropy 0.826087 0.869484
1250 0.300000 4 50 log_loss 0.795031 0.851461
1251 0.300000 4 100 gini 0.834369 0.868863
1252 0.300000 4 100 entropy 0.836439 0.867620
1253 0.300000 4 100 log_loss 0.836439 0.869484
1254 0.300000 4 200 gini 0.826087 0.868863
1255 0.300000 4 200 entropy 0.836439 0.868863
1256 0.300000 4 200 log_loss 0.846791 0.872592
1257 0.300000 4 300 gini 0.840580 0.875699
1258 0.300000 4 300 entropy 0.844720 0.868241
1259 0.300000 4 300 log_loss 0.848861 0.879428
1260 0.300000 5 10 gini 0.786749 0.855811
1261 0.300000 5 10 entropy 0.813665 0.852704
1262 0.300000 5 10 log_loss 0.751553 0.816656
1263 0.300000 5 50 gini 0.834369 0.892480
1264 0.300000 5 50 entropy 0.834369 0.890615
1265 0.300000 5 50 log_loss 0.834369 0.896209
1266 0.300000 5 100 gini 0.832298 0.907396
1267 0.300000 5 100 entropy 0.842650 0.893101
1268 0.300000 5 100 log_loss 0.834369 0.894966
1269 0.300000 5 200 gini 0.844720 0.909882
1270 0.300000 5 200 entropy 0.836439 0.890615
1271 0.300000 5 200 log_loss 0.861284 0.904910
1272 0.300000 5 300 gini 0.850932 0.906153
1273 0.300000 5 300 entropy 0.848861 0.894966
1274 0.300000 5 300 log_loss 0.848861 0.901802
1275 0.300000 6 10 gini 0.757764 0.867620
1276 0.300000 6 10 entropy 0.770186 0.854568
1277 0.300000 6 10 log_loss 0.780538 0.852704
1278 0.300000 6 50 gini 0.842650 0.917961
1279 0.300000 6 50 entropy 0.840580 0.916718
1280 0.300000 6 50 log_loss 0.863354 0.927906
1281 0.300000 6 100 gini 0.869565 0.931635
1282 0.300000 6 100 entropy 0.865424 0.934742
1283 0.300000 6 100 log_loss 0.853002 0.927906
1284 0.300000 6 200 gini 0.850932 0.925420
1285 0.300000 6 200 entropy 0.857143 0.926041
1286 0.300000 6 200 log_loss 0.857143 0.931635
1287 0.300000 6 300 gini 0.861284 0.931635
1288 0.300000 6 300 entropy 0.850932 0.925420
1289 0.300000 6 300 log_loss 0.873706 0.937228
1290 0.300000 7 10 gini 0.770186 0.881914
1291 0.300000 7 10 entropy 0.774327 0.898073
1292 0.300000 7 10 log_loss 0.747412 0.898073
1293 0.300000 7 50 gini 0.865424 0.945929
1294 0.300000 7 50 entropy 0.840580 0.939093
1295 0.300000 7 50 log_loss 0.848861 0.934742
1296 0.300000 7 100 gini 0.869565 0.947794
1297 0.300000 7 100 entropy 0.859213 0.945929
1298 0.300000 7 100 log_loss 0.861284 0.945308
1299 0.300000 7 200 gini 0.871636 0.950280
1300 0.300000 7 200 entropy 0.863354 0.947172
1301 0.300000 7 200 log_loss 0.875776 0.949658
1302 0.300000 7 300 gini 0.873706 0.952144
1303 0.300000 7 300 entropy 0.863354 0.946551
1304 0.300000 7 300 log_loss 0.861284 0.949037
1305 0.300000 8 10 gini 0.780538 0.909882
1306 0.300000 8 10 entropy 0.788820 0.919826
1307 0.300000 8 10 log_loss 0.786749 0.907396
1308 0.300000 8 50 gini 0.844720 0.947172
1309 0.300000 8 50 entropy 0.865424 0.955252
1310 0.300000 8 50 log_loss 0.863354 0.953387
1311 0.300000 8 100 gini 0.863354 0.952766
1312 0.300000 8 100 entropy 0.892340 0.965196
1313 0.300000 8 100 log_loss 0.873706 0.959602
1314 0.300000 8 200 gini 0.861284 0.954009
1315 0.300000 8 200 entropy 0.875776 0.958981
1316 0.300000 8 200 log_loss 0.867495 0.958981
1317 0.300000 8 300 gini 0.873706 0.957738
1318 0.300000 8 300 entropy 0.871636 0.958981
1319 0.300000 8 300 log_loss 0.879917 0.962710
1320 0.300000 9 10 gini 0.799172 0.922312
1321 0.300000 9 10 entropy 0.830228 0.939093
1322 0.300000 9 10 log_loss 0.795031 0.922933
1323 0.300000 9 50 gini 0.869565 0.959602
1324 0.300000 9 50 entropy 0.877847 0.962710
1325 0.300000 9 50 log_loss 0.857143 0.957116
1326 0.300000 9 100 gini 0.848861 0.954009
1327 0.300000 9 100 entropy 0.881988 0.964574
1328 0.300000 9 100 log_loss 0.879917 0.963331
1329 0.300000 9 200 gini 0.871636 0.961467
1330 0.300000 9 200 entropy 0.886128 0.964574
1331 0.300000 9 200 log_loss 0.871636 0.961467
1332 0.300000 9 300 gini 0.875776 0.962088
1333 0.300000 9 300 entropy 0.873706 0.962088
1334 0.300000 9 300 log_loss 0.875776 0.962710
1335 0.300000 10 10 gini 0.809524 0.931635
1336 0.300000 10 10 entropy 0.840580 0.947172
1337 0.300000 10 10 log_loss 0.768116 0.916718
1338 0.300000 10 50 gini 0.877847 0.962710
1339 0.300000 10 50 entropy 0.871636 0.961467
1340 0.300000 10 50 log_loss 0.857143 0.957116
1341 0.300000 10 100 gini 0.869565 0.960224
1342 0.300000 10 100 entropy 0.877847 0.963331
1343 0.300000 10 100 log_loss 0.881988 0.964574
1344 0.300000 10 200 gini 0.881988 0.964574
1345 0.300000 10 200 entropy 0.881988 0.964574
1346 0.300000 10 200 log_loss 0.886128 0.965817
1347 0.300000 10 300 gini 0.875776 0.962710
1348 0.300000 10 300 entropy 0.875776 0.962710
1349 0.300000 10 300 log_loss 0.881988 0.964574
1350 0.300000 11 10 gini 0.782609 0.931013
1351 0.300000 11 10 entropy 0.824017 0.940957
1352 0.300000 11 10 log_loss 0.795031 0.933499
1353 0.300000 11 50 gini 0.873706 0.962088
1354 0.300000 11 50 entropy 0.873706 0.962088
1355 0.300000 11 50 log_loss 0.865424 0.959602
1356 0.300000 11 100 gini 0.890269 0.967060
1357 0.300000 11 100 entropy 0.881988 0.964574
1358 0.300000 11 100 log_loss 0.881988 0.964574
1359 0.300000 11 200 gini 0.884058 0.965196
1360 0.300000 11 200 entropy 0.896480 0.968925
1361 0.300000 11 200 log_loss 0.890269 0.967060
1362 0.300000 11 300 gini 0.877847 0.963331
1363 0.300000 11 300 entropy 0.873706 0.962088
1364 0.300000 11 300 log_loss 0.886128 0.965817
1365 0.300000 12 10 gini 0.737060 0.913611
1366 0.300000 12 10 entropy 0.805383 0.939714
1367 0.300000 12 10 log_loss 0.786749 0.931013
1368 0.300000 12 50 gini 0.859213 0.957738
1369 0.300000 12 50 entropy 0.873706 0.962088
1370 0.300000 12 50 log_loss 0.869565 0.960845
1371 0.300000 12 100 gini 0.884058 0.965196
1372 0.300000 12 100 entropy 0.896480 0.968925
1373 0.300000 12 100 log_loss 0.894410 0.968303
1374 0.300000 12 200 gini 0.873706 0.962088
1375 0.300000 12 200 entropy 0.879917 0.963953
1376 0.300000 12 200 log_loss 0.888199 0.966439
1377 0.300000 12 300 gini 0.873706 0.962088
1378 0.300000 12 300 entropy 0.873706 0.962088
1379 0.300000 12 300 log_loss 0.890269 0.967060
1380 0.300000 13 10 gini 0.815735 0.942200
1381 0.300000 13 10 entropy 0.805383 0.940957
1382 0.300000 13 10 log_loss 0.786749 0.931635
1383 0.300000 13 50 gini 0.859213 0.957738
1384 0.300000 13 50 entropy 0.846791 0.954009
1385 0.300000 13 50 log_loss 0.869565 0.960845
1386 0.300000 13 100 gini 0.861284 0.958359
1387 0.300000 13 100 entropy 0.869565 0.960845
1388 0.300000 13 100 log_loss 0.892340 0.967682
1389 0.300000 13 200 gini 0.890269 0.967060
1390 0.300000 13 200 entropy 0.890269 0.967060
1391 0.300000 13 200 log_loss 0.896480 0.968925
1392 0.300000 13 300 gini 0.888199 0.966439
1393 0.300000 13 300 entropy 0.884058 0.965196
1394 0.300000 13 300 log_loss 0.892340 0.967682
1395 0.300000 14 10 gini 0.817805 0.940957
1396 0.300000 14 10 entropy 0.770186 0.928527
1397 0.300000 14 10 log_loss 0.838509 0.948415
1398 0.300000 14 50 gini 0.853002 0.955873
1399 0.300000 14 50 entropy 0.863354 0.958981
1400 0.300000 14 50 log_loss 0.865424 0.959602
1401 0.300000 14 100 gini 0.867495 0.960224
1402 0.300000 14 100 entropy 0.869565 0.960845
1403 0.300000 14 100 log_loss 0.886128 0.965817
1404 0.300000 14 200 gini 0.892340 0.967682
1405 0.300000 14 200 entropy 0.884058 0.965196
1406 0.300000 14 200 log_loss 0.892340 0.967682
1407 0.300000 14 300 gini 0.886128 0.965817
1408 0.300000 14 300 entropy 0.881988 0.964574
1409 0.300000 14 300 log_loss 0.884058 0.965196
1410 0.300000 15 10 gini 0.826087 0.945308
1411 0.300000 15 10 entropy 0.792961 0.935364
1412 0.300000 15 10 log_loss 0.797101 0.937850
1413 0.300000 15 50 gini 0.865424 0.959602
1414 0.300000 15 50 entropy 0.857143 0.957116
1415 0.300000 15 50 log_loss 0.877847 0.963331
1416 0.300000 15 100 gini 0.867495 0.960224
1417 0.300000 15 100 entropy 0.888199 0.966439
1418 0.300000 15 100 log_loss 0.873706 0.962088
1419 0.300000 15 200 gini 0.896480 0.968925
1420 0.300000 15 200 entropy 0.888199 0.966439
1421 0.300000 15 200 log_loss 0.879917 0.963953
1422 0.300000 15 300 gini 0.892340 0.967682
1423 0.300000 15 300 entropy 0.877847 0.963331
1424 0.300000 15 300 log_loss 0.888199 0.966439
1425 0.300000 16 10 gini 0.772257 0.929770
1426 0.300000 16 10 entropy 0.815735 0.942822
1427 0.300000 16 10 log_loss 0.803313 0.936607
1428 0.300000 16 50 gini 0.859213 0.957738
1429 0.300000 16 50 entropy 0.884058 0.965196
1430 0.300000 16 50 log_loss 0.886128 0.965817
1431 0.300000 16 100 gini 0.884058 0.965196
1432 0.300000 16 100 entropy 0.873706 0.962088
1433 0.300000 16 100 log_loss 0.890269 0.967060
1434 0.300000 16 200 gini 0.881988 0.964574
1435 0.300000 16 200 entropy 0.890269 0.967060
1436 0.300000 16 200 log_loss 0.879917 0.963953
1437 0.300000 16 300 gini 0.884058 0.965196
1438 0.300000 16 300 entropy 0.884058 0.965196
1439 0.300000 16 300 log_loss 0.886128 0.965817
1440 0.300000 17 10 gini 0.788820 0.934121
1441 0.300000 17 10 entropy 0.799172 0.937850
1442 0.300000 17 10 log_loss 0.786749 0.933499
1443 0.300000 17 50 gini 0.871636 0.961467
1444 0.300000 17 50 entropy 0.879917 0.963953
1445 0.300000 17 50 log_loss 0.881988 0.964574
1446 0.300000 17 100 gini 0.884058 0.965196
1447 0.300000 17 100 entropy 0.875776 0.962710
1448 0.300000 17 100 log_loss 0.890269 0.967060
1449 0.300000 17 200 gini 0.869565 0.960845
1450 0.300000 17 200 entropy 0.884058 0.965196
1451 0.300000 17 200 log_loss 0.871636 0.961467
1452 0.300000 17 300 gini 0.877847 0.963331
1453 0.300000 17 300 entropy 0.892340 0.967682
1454 0.300000 17 300 log_loss 0.881988 0.964574
1455 0.300000 18 10 gini 0.803313 0.939714
1456 0.300000 18 10 entropy 0.792961 0.932878
1457 0.300000 18 10 log_loss 0.782609 0.930392
1458 0.300000 18 50 gini 0.863354 0.958981
1459 0.300000 18 50 entropy 0.892340 0.967682
1460 0.300000 18 50 log_loss 0.877847 0.963331
1461 0.300000 18 100 gini 0.871636 0.961467
1462 0.300000 18 100 entropy 0.879917 0.963953
1463 0.300000 18 100 log_loss 0.857143 0.957116
1464 0.300000 18 200 gini 0.871636 0.961467
1465 0.300000 18 200 entropy 0.892340 0.967682
1466 0.300000 18 200 log_loss 0.894410 0.968303
1467 0.300000 18 300 gini 0.879917 0.963953
1468 0.300000 18 300 entropy 0.890269 0.967060
1469 0.300000 18 300 log_loss 0.890269 0.967060
1470 0.300000 19 10 gini 0.782609 0.932256
1471 0.300000 19 10 entropy 0.815735 0.941579
1472 0.300000 19 10 log_loss 0.792961 0.937850
1473 0.300000 19 50 gini 0.871636 0.961467
1474 0.300000 19 50 entropy 0.869565 0.960845
1475 0.300000 19 50 log_loss 0.848861 0.954630
1476 0.300000 19 100 gini 0.867495 0.960224
1477 0.300000 19 100 entropy 0.890269 0.967060
1478 0.300000 19 100 log_loss 0.865424 0.959602
1479 0.300000 19 200 gini 0.867495 0.960224
1480 0.300000 19 200 entropy 0.890269 0.967060
1481 0.300000 19 200 log_loss 0.892340 0.967682
1482 0.300000 19 300 gini 0.877847 0.963331
1483 0.300000 19 300 entropy 0.890269 0.967060
1484 0.300000 19 300 log_loss 0.888199 0.966439
1485 0.300000 20 10 gini 0.788820 0.933499
1486 0.300000 20 10 entropy 0.768116 0.927284
1487 0.300000 20 10 log_loss 0.805383 0.939714
1488 0.300000 20 50 gini 0.873706 0.962088
1489 0.300000 20 50 entropy 0.873706 0.962088
1490 0.300000 20 50 log_loss 0.865424 0.959602
1491 0.300000 20 100 gini 0.879917 0.963953
1492 0.300000 20 100 entropy 0.873706 0.962088
1493 0.300000 20 100 log_loss 0.879917 0.963953
1494 0.300000 20 200 gini 0.881988 0.964574
1495 0.300000 20 200 entropy 0.877847 0.963331
1496 0.300000 20 200 log_loss 0.884058 0.965196
1497 0.300000 20 300 gini 0.886128 0.965817
1498 0.300000 20 300 entropy 0.879917 0.963953
1499 0.300000 20 300 log_loss 0.890269 0.967060
Row=264, Test_size=0.1, Max_depth=18, n_estimators=200, criterion=gini, Accuracy=0.925466, Score=0.992542
In [49]:
X_train, X_test, y_train, y_test = train_test_split(Xr, yr, test_size=0.1, random_state=0)
In [50]:
clf_rf = RandomForestClassifier(max_depth=19, n_estimators=200, criterion='gini')
clf_rf.fit(X_train, y_train)
y_pred = clf_rf.predict(X_test)
In [51]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_rf.score(Xr, yr))
Accuracy =  0.906832298136646 
Score =  0.9906774394033562

SVM¶

In [52]:
Xs = df_train.drop('price_range', axis=1)
ys = df_train.price_range.values.reshape(-1, 1)
In [53]:
def svc(Xs, ys, Testsize):
    df_evaluation_svm = pd.DataFrame()

    C_values = [0.1, 1, 10]
    kernel_values = ['linear', 'rbf', 'poly', 'sigmoid']
    decision_function_shape_values = ['ovo', 'ovr']
    degree_values = [2, 3, 4]
    gamma_values = ['scale', 'auto']

    for C in C_values:
        for kernel in kernel_values:
            for decision_function_shape in decision_function_shape_values:
                for degree in degree_values:
                    for gamma in gamma_values:
                        for x in Testsize:
                            X_train, X_test, y_train, y_test = train_test_split(Xs, ys, test_size=x, random_state=0)
                            svm = SVC(C=C, kernel=kernel, decision_function_shape=decision_function_shape, degree=degree, gamma=gamma)
                            svm.fit(X_train, y_train)
                            y_pred = svm.predict(X_test)
                            dict = {'C': C, 'kernel': kernel, 'decision_function_shape': decision_function_shape, 'degree': degree, 'gamma': gamma, 'Test_size': x, 'acc': metrics.accuracy_score(y_test, y_pred), 'score': svm.score(Xs, ys)}
                            df_evaluation_svm = pd.concat([df_evaluation_svm, pd.DataFrame(dict, index=[0])], ignore_index=True)

    return (df_evaluation_svm)


df_evaluation_svm = svc(Xs, ys, [.1, .15, .2, .25, .3])
df_evaluation_svm.style.apply(highlight_max)
Out[53]:
  C kernel decision_function_shape degree gamma Test_size acc score
0 0.100000 linear ovo 2 scale 0.100000 0.981366 0.987570
1 0.100000 linear ovo 2 scale 0.150000 0.983471 0.988813
2 0.100000 linear ovo 2 scale 0.200000 0.965839 0.987570
3 0.100000 linear ovo 2 scale 0.250000 0.965261 0.985705
4 0.100000 linear ovo 2 scale 0.300000 0.964803 0.983841
5 0.100000 linear ovo 2 auto 0.100000 0.981366 0.987570
6 0.100000 linear ovo 2 auto 0.150000 0.983471 0.988813
7 0.100000 linear ovo 2 auto 0.200000 0.965839 0.987570
8 0.100000 linear ovo 2 auto 0.250000 0.965261 0.985705
9 0.100000 linear ovo 2 auto 0.300000 0.964803 0.983841
10 0.100000 linear ovo 3 scale 0.100000 0.981366 0.987570
11 0.100000 linear ovo 3 scale 0.150000 0.983471 0.988813
12 0.100000 linear ovo 3 scale 0.200000 0.965839 0.987570
13 0.100000 linear ovo 3 scale 0.250000 0.965261 0.985705
14 0.100000 linear ovo 3 scale 0.300000 0.964803 0.983841
15 0.100000 linear ovo 3 auto 0.100000 0.981366 0.987570
16 0.100000 linear ovo 3 auto 0.150000 0.983471 0.988813
17 0.100000 linear ovo 3 auto 0.200000 0.965839 0.987570
18 0.100000 linear ovo 3 auto 0.250000 0.965261 0.985705
19 0.100000 linear ovo 3 auto 0.300000 0.964803 0.983841
20 0.100000 linear ovo 4 scale 0.100000 0.981366 0.987570
21 0.100000 linear ovo 4 scale 0.150000 0.983471 0.988813
22 0.100000 linear ovo 4 scale 0.200000 0.965839 0.987570
23 0.100000 linear ovo 4 scale 0.250000 0.965261 0.985705
24 0.100000 linear ovo 4 scale 0.300000 0.964803 0.983841
25 0.100000 linear ovo 4 auto 0.100000 0.981366 0.987570
26 0.100000 linear ovo 4 auto 0.150000 0.983471 0.988813
27 0.100000 linear ovo 4 auto 0.200000 0.965839 0.987570
28 0.100000 linear ovo 4 auto 0.250000 0.965261 0.985705
29 0.100000 linear ovo 4 auto 0.300000 0.964803 0.983841
30 0.100000 linear ovr 2 scale 0.100000 0.981366 0.987570
31 0.100000 linear ovr 2 scale 0.150000 0.983471 0.988813
32 0.100000 linear ovr 2 scale 0.200000 0.965839 0.987570
33 0.100000 linear ovr 2 scale 0.250000 0.965261 0.985705
34 0.100000 linear ovr 2 scale 0.300000 0.964803 0.983841
35 0.100000 linear ovr 2 auto 0.100000 0.981366 0.987570
36 0.100000 linear ovr 2 auto 0.150000 0.983471 0.988813
37 0.100000 linear ovr 2 auto 0.200000 0.965839 0.987570
38 0.100000 linear ovr 2 auto 0.250000 0.965261 0.985705
39 0.100000 linear ovr 2 auto 0.300000 0.964803 0.983841
40 0.100000 linear ovr 3 scale 0.100000 0.981366 0.987570
41 0.100000 linear ovr 3 scale 0.150000 0.983471 0.988813
42 0.100000 linear ovr 3 scale 0.200000 0.965839 0.987570
43 0.100000 linear ovr 3 scale 0.250000 0.965261 0.985705
44 0.100000 linear ovr 3 scale 0.300000 0.964803 0.983841
45 0.100000 linear ovr 3 auto 0.100000 0.981366 0.987570
46 0.100000 linear ovr 3 auto 0.150000 0.983471 0.988813
47 0.100000 linear ovr 3 auto 0.200000 0.965839 0.987570
48 0.100000 linear ovr 3 auto 0.250000 0.965261 0.985705
49 0.100000 linear ovr 3 auto 0.300000 0.964803 0.983841
50 0.100000 linear ovr 4 scale 0.100000 0.981366 0.987570
51 0.100000 linear ovr 4 scale 0.150000 0.983471 0.988813
52 0.100000 linear ovr 4 scale 0.200000 0.965839 0.987570
53 0.100000 linear ovr 4 scale 0.250000 0.965261 0.985705
54 0.100000 linear ovr 4 scale 0.300000 0.964803 0.983841
55 0.100000 linear ovr 4 auto 0.100000 0.981366 0.987570
56 0.100000 linear ovr 4 auto 0.150000 0.983471 0.988813
57 0.100000 linear ovr 4 auto 0.200000 0.965839 0.987570
58 0.100000 linear ovr 4 auto 0.250000 0.965261 0.985705
59 0.100000 linear ovr 4 auto 0.300000 0.964803 0.983841
60 0.100000 rbf ovo 2 scale 0.100000 0.931677 0.899938
61 0.100000 rbf ovo 2 scale 0.150000 0.913223 0.897452
62 0.100000 rbf ovo 2 scale 0.200000 0.891304 0.893101
63 0.100000 rbf ovo 2 scale 0.250000 0.883375 0.889372
64 0.100000 rbf ovo 2 scale 0.300000 0.888199 0.891237
65 0.100000 rbf ovo 2 auto 0.100000 0.242236 0.253574
66 0.100000 rbf ovo 2 auto 0.150000 0.252066 0.253574
67 0.100000 rbf ovo 2 auto 0.200000 0.214286 0.249223
68 0.100000 rbf ovo 2 auto 0.250000 0.210918 0.249223
69 0.100000 rbf ovo 2 auto 0.300000 0.229814 0.249223
70 0.100000 rbf ovo 3 scale 0.100000 0.931677 0.899938
71 0.100000 rbf ovo 3 scale 0.150000 0.913223 0.897452
72 0.100000 rbf ovo 3 scale 0.200000 0.891304 0.893101
73 0.100000 rbf ovo 3 scale 0.250000 0.883375 0.889372
74 0.100000 rbf ovo 3 scale 0.300000 0.888199 0.891237
75 0.100000 rbf ovo 3 auto 0.100000 0.242236 0.253574
76 0.100000 rbf ovo 3 auto 0.150000 0.252066 0.253574
77 0.100000 rbf ovo 3 auto 0.200000 0.214286 0.249223
78 0.100000 rbf ovo 3 auto 0.250000 0.210918 0.249223
79 0.100000 rbf ovo 3 auto 0.300000 0.229814 0.249223
80 0.100000 rbf ovo 4 scale 0.100000 0.931677 0.899938
81 0.100000 rbf ovo 4 scale 0.150000 0.913223 0.897452
82 0.100000 rbf ovo 4 scale 0.200000 0.891304 0.893101
83 0.100000 rbf ovo 4 scale 0.250000 0.883375 0.889372
84 0.100000 rbf ovo 4 scale 0.300000 0.888199 0.891237
85 0.100000 rbf ovo 4 auto 0.100000 0.242236 0.253574
86 0.100000 rbf ovo 4 auto 0.150000 0.252066 0.253574
87 0.100000 rbf ovo 4 auto 0.200000 0.214286 0.249223
88 0.100000 rbf ovo 4 auto 0.250000 0.210918 0.249223
89 0.100000 rbf ovo 4 auto 0.300000 0.229814 0.249223
90 0.100000 rbf ovr 2 scale 0.100000 0.931677 0.899938
91 0.100000 rbf ovr 2 scale 0.150000 0.913223 0.897452
92 0.100000 rbf ovr 2 scale 0.200000 0.891304 0.893101
93 0.100000 rbf ovr 2 scale 0.250000 0.883375 0.889372
94 0.100000 rbf ovr 2 scale 0.300000 0.888199 0.891237
95 0.100000 rbf ovr 2 auto 0.100000 0.242236 0.253574
96 0.100000 rbf ovr 2 auto 0.150000 0.252066 0.253574
97 0.100000 rbf ovr 2 auto 0.200000 0.214286 0.249223
98 0.100000 rbf ovr 2 auto 0.250000 0.210918 0.249223
99 0.100000 rbf ovr 2 auto 0.300000 0.229814 0.249223
100 0.100000 rbf ovr 3 scale 0.100000 0.931677 0.899938
101 0.100000 rbf ovr 3 scale 0.150000 0.913223 0.897452
102 0.100000 rbf ovr 3 scale 0.200000 0.891304 0.893101
103 0.100000 rbf ovr 3 scale 0.250000 0.883375 0.889372
104 0.100000 rbf ovr 3 scale 0.300000 0.888199 0.891237
105 0.100000 rbf ovr 3 auto 0.100000 0.242236 0.253574
106 0.100000 rbf ovr 3 auto 0.150000 0.252066 0.253574
107 0.100000 rbf ovr 3 auto 0.200000 0.214286 0.249223
108 0.100000 rbf ovr 3 auto 0.250000 0.210918 0.249223
109 0.100000 rbf ovr 3 auto 0.300000 0.229814 0.249223
110 0.100000 rbf ovr 4 scale 0.100000 0.931677 0.899938
111 0.100000 rbf ovr 4 scale 0.150000 0.913223 0.897452
112 0.100000 rbf ovr 4 scale 0.200000 0.891304 0.893101
113 0.100000 rbf ovr 4 scale 0.250000 0.883375 0.889372
114 0.100000 rbf ovr 4 scale 0.300000 0.888199 0.891237
115 0.100000 rbf ovr 4 auto 0.100000 0.242236 0.253574
116 0.100000 rbf ovr 4 auto 0.150000 0.252066 0.253574
117 0.100000 rbf ovr 4 auto 0.200000 0.214286 0.249223
118 0.100000 rbf ovr 4 auto 0.250000 0.210918 0.249223
119 0.100000 rbf ovr 4 auto 0.300000 0.229814 0.249223
120 0.100000 poly ovo 2 scale 0.100000 0.950311 0.934121
121 0.100000 poly ovo 2 scale 0.150000 0.942149 0.933499
122 0.100000 poly ovo 2 scale 0.200000 0.931677 0.930392
123 0.100000 poly ovo 2 scale 0.250000 0.915633 0.929149
124 0.100000 poly ovo 2 scale 0.300000 0.913043 0.920447
125 0.100000 poly ovo 2 auto 0.100000 0.975155 0.997514
126 0.100000 poly ovo 2 auto 0.150000 0.975207 0.996271
127 0.100000 poly ovo 2 auto 0.200000 0.968944 0.993785
128 0.100000 poly ovo 2 auto 0.250000 0.960298 0.990056
129 0.100000 poly ovo 2 auto 0.300000 0.960663 0.988191
130 0.100000 poly ovo 3 scale 0.100000 0.944099 0.934121
131 0.100000 poly ovo 3 scale 0.150000 0.946281 0.932878
132 0.100000 poly ovo 3 scale 0.200000 0.937888 0.933499
133 0.100000 poly ovo 3 scale 0.250000 0.928040 0.928527
134 0.100000 poly ovo 3 scale 0.300000 0.923395 0.922933
135 0.100000 poly ovo 3 auto 0.100000 0.968944 0.996892
136 0.100000 poly ovo 3 auto 0.150000 0.975207 0.996271
137 0.100000 poly ovo 3 auto 0.200000 0.962733 0.992542
138 0.100000 poly ovo 3 auto 0.250000 0.957816 0.989434
139 0.100000 poly ovo 3 auto 0.300000 0.960663 0.988191
140 0.100000 poly ovo 4 scale 0.100000 0.937888 0.933499
141 0.100000 poly ovo 4 scale 0.150000 0.946281 0.932878
142 0.100000 poly ovo 4 scale 0.200000 0.947205 0.932256
143 0.100000 poly ovo 4 scale 0.250000 0.940447 0.931635
144 0.100000 poly ovo 4 scale 0.300000 0.933747 0.926663
145 0.100000 poly ovo 4 auto 0.100000 0.968944 0.996892
146 0.100000 poly ovo 4 auto 0.150000 0.971074 0.995649
147 0.100000 poly ovo 4 auto 0.200000 0.962733 0.992542
148 0.100000 poly ovo 4 auto 0.250000 0.960298 0.990056
149 0.100000 poly ovo 4 auto 0.300000 0.958592 0.987570
150 0.100000 poly ovr 2 scale 0.100000 0.950311 0.934121
151 0.100000 poly ovr 2 scale 0.150000 0.942149 0.933499
152 0.100000 poly ovr 2 scale 0.200000 0.931677 0.930392
153 0.100000 poly ovr 2 scale 0.250000 0.915633 0.929149
154 0.100000 poly ovr 2 scale 0.300000 0.913043 0.920447
155 0.100000 poly ovr 2 auto 0.100000 0.975155 0.997514
156 0.100000 poly ovr 2 auto 0.150000 0.975207 0.996271
157 0.100000 poly ovr 2 auto 0.200000 0.968944 0.993785
158 0.100000 poly ovr 2 auto 0.250000 0.960298 0.990056
159 0.100000 poly ovr 2 auto 0.300000 0.960663 0.988191
160 0.100000 poly ovr 3 scale 0.100000 0.944099 0.934121
161 0.100000 poly ovr 3 scale 0.150000 0.946281 0.932878
162 0.100000 poly ovr 3 scale 0.200000 0.937888 0.933499
163 0.100000 poly ovr 3 scale 0.250000 0.928040 0.928527
164 0.100000 poly ovr 3 scale 0.300000 0.923395 0.922933
165 0.100000 poly ovr 3 auto 0.100000 0.968944 0.996892
166 0.100000 poly ovr 3 auto 0.150000 0.975207 0.996271
167 0.100000 poly ovr 3 auto 0.200000 0.962733 0.992542
168 0.100000 poly ovr 3 auto 0.250000 0.957816 0.989434
169 0.100000 poly ovr 3 auto 0.300000 0.960663 0.988191
170 0.100000 poly ovr 4 scale 0.100000 0.937888 0.933499
171 0.100000 poly ovr 4 scale 0.150000 0.946281 0.932878
172 0.100000 poly ovr 4 scale 0.200000 0.947205 0.932256
173 0.100000 poly ovr 4 scale 0.250000 0.940447 0.931635
174 0.100000 poly ovr 4 scale 0.300000 0.933747 0.926663
175 0.100000 poly ovr 4 auto 0.100000 0.968944 0.996892
176 0.100000 poly ovr 4 auto 0.150000 0.971074 0.995649
177 0.100000 poly ovr 4 auto 0.200000 0.962733 0.992542
178 0.100000 poly ovr 4 auto 0.250000 0.960298 0.990056
179 0.100000 poly ovr 4 auto 0.300000 0.958592 0.987570
180 0.100000 sigmoid ovo 2 scale 0.100000 0.409938 0.423866
181 0.100000 sigmoid ovo 2 scale 0.150000 0.429752 0.442511
182 0.100000 sigmoid ovo 2 scale 0.200000 0.440994 0.444997
183 0.100000 sigmoid ovo 2 scale 0.250000 0.233251 0.254195
184 0.100000 sigmoid ovo 2 scale 0.300000 0.229814 0.240522
185 0.100000 sigmoid ovo 2 auto 0.100000 0.242236 0.253574
186 0.100000 sigmoid ovo 2 auto 0.150000 0.252066 0.253574
187 0.100000 sigmoid ovo 2 auto 0.200000 0.214286 0.249223
188 0.100000 sigmoid ovo 2 auto 0.250000 0.210918 0.249223
189 0.100000 sigmoid ovo 2 auto 0.300000 0.229814 0.249223
190 0.100000 sigmoid ovo 3 scale 0.100000 0.409938 0.423866
191 0.100000 sigmoid ovo 3 scale 0.150000 0.429752 0.442511
192 0.100000 sigmoid ovo 3 scale 0.200000 0.440994 0.444997
193 0.100000 sigmoid ovo 3 scale 0.250000 0.233251 0.254195
194 0.100000 sigmoid ovo 3 scale 0.300000 0.229814 0.240522
195 0.100000 sigmoid ovo 3 auto 0.100000 0.242236 0.253574
196 0.100000 sigmoid ovo 3 auto 0.150000 0.252066 0.253574
197 0.100000 sigmoid ovo 3 auto 0.200000 0.214286 0.249223
198 0.100000 sigmoid ovo 3 auto 0.250000 0.210918 0.249223
199 0.100000 sigmoid ovo 3 auto 0.300000 0.229814 0.249223
200 0.100000 sigmoid ovo 4 scale 0.100000 0.409938 0.423866
201 0.100000 sigmoid ovo 4 scale 0.150000 0.429752 0.442511
202 0.100000 sigmoid ovo 4 scale 0.200000 0.440994 0.444997
203 0.100000 sigmoid ovo 4 scale 0.250000 0.233251 0.254195
204 0.100000 sigmoid ovo 4 scale 0.300000 0.229814 0.240522
205 0.100000 sigmoid ovo 4 auto 0.100000 0.242236 0.253574
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528 10.000000 linear ovr 3 auto 0.250000 0.962779 0.988813
529 10.000000 linear ovr 3 auto 0.300000 0.966874 0.988191
530 10.000000 linear ovr 4 scale 0.100000 0.968944 0.984462
531 10.000000 linear ovr 4 scale 0.150000 0.975207 0.986327
532 10.000000 linear ovr 4 scale 0.200000 0.962733 0.988191
533 10.000000 linear ovr 4 scale 0.250000 0.962779 0.988813
534 10.000000 linear ovr 4 scale 0.300000 0.966874 0.988191
535 10.000000 linear ovr 4 auto 0.100000 0.968944 0.984462
536 10.000000 linear ovr 4 auto 0.150000 0.975207 0.986327
537 10.000000 linear ovr 4 auto 0.200000 0.962733 0.988191
538 10.000000 linear ovr 4 auto 0.250000 0.962779 0.988813
539 10.000000 linear ovr 4 auto 0.300000 0.966874 0.988191
540 10.000000 rbf ovo 2 scale 0.100000 0.962733 0.963953
541 10.000000 rbf ovo 2 scale 0.150000 0.966942 0.966439
542 10.000000 rbf ovo 2 scale 0.200000 0.962733 0.967682
543 10.000000 rbf ovo 2 scale 0.250000 0.952854 0.967060
544 10.000000 rbf ovo 2 scale 0.300000 0.956522 0.964574
545 10.000000 rbf ovo 2 auto 0.100000 0.242236 0.924177
546 10.000000 rbf ovo 2 auto 0.150000 0.252066 0.887508
547 10.000000 rbf ovo 2 auto 0.200000 0.214286 0.842759
548 10.000000 rbf ovo 2 auto 0.250000 0.210918 0.802362
549 10.000000 rbf ovo 2 auto 0.300000 0.229814 0.768800
550 10.000000 rbf ovo 3 scale 0.100000 0.962733 0.963953
551 10.000000 rbf ovo 3 scale 0.150000 0.966942 0.966439
552 10.000000 rbf ovo 3 scale 0.200000 0.962733 0.967682
553 10.000000 rbf ovo 3 scale 0.250000 0.952854 0.967060
554 10.000000 rbf ovo 3 scale 0.300000 0.956522 0.964574
555 10.000000 rbf ovo 3 auto 0.100000 0.242236 0.924177
556 10.000000 rbf ovo 3 auto 0.150000 0.252066 0.887508
557 10.000000 rbf ovo 3 auto 0.200000 0.214286 0.842759
558 10.000000 rbf ovo 3 auto 0.250000 0.210918 0.802362
559 10.000000 rbf ovo 3 auto 0.300000 0.229814 0.768800
560 10.000000 rbf ovo 4 scale 0.100000 0.962733 0.963953
561 10.000000 rbf ovo 4 scale 0.150000 0.966942 0.966439
562 10.000000 rbf ovo 4 scale 0.200000 0.962733 0.967682
563 10.000000 rbf ovo 4 scale 0.250000 0.952854 0.967060
564 10.000000 rbf ovo 4 scale 0.300000 0.956522 0.964574
565 10.000000 rbf ovo 4 auto 0.100000 0.242236 0.924177
566 10.000000 rbf ovo 4 auto 0.150000 0.252066 0.887508
567 10.000000 rbf ovo 4 auto 0.200000 0.214286 0.842759
568 10.000000 rbf ovo 4 auto 0.250000 0.210918 0.802362
569 10.000000 rbf ovo 4 auto 0.300000 0.229814 0.768800
570 10.000000 rbf ovr 2 scale 0.100000 0.962733 0.963953
571 10.000000 rbf ovr 2 scale 0.150000 0.966942 0.966439
572 10.000000 rbf ovr 2 scale 0.200000 0.962733 0.967682
573 10.000000 rbf ovr 2 scale 0.250000 0.952854 0.967060
574 10.000000 rbf ovr 2 scale 0.300000 0.956522 0.964574
575 10.000000 rbf ovr 2 auto 0.100000 0.242236 0.924177
576 10.000000 rbf ovr 2 auto 0.150000 0.252066 0.887508
577 10.000000 rbf ovr 2 auto 0.200000 0.214286 0.842759
578 10.000000 rbf ovr 2 auto 0.250000 0.210918 0.802362
579 10.000000 rbf ovr 2 auto 0.300000 0.229814 0.768800
580 10.000000 rbf ovr 3 scale 0.100000 0.962733 0.963953
581 10.000000 rbf ovr 3 scale 0.150000 0.966942 0.966439
582 10.000000 rbf ovr 3 scale 0.200000 0.962733 0.967682
583 10.000000 rbf ovr 3 scale 0.250000 0.952854 0.967060
584 10.000000 rbf ovr 3 scale 0.300000 0.956522 0.964574
585 10.000000 rbf ovr 3 auto 0.100000 0.242236 0.924177
586 10.000000 rbf ovr 3 auto 0.150000 0.252066 0.887508
587 10.000000 rbf ovr 3 auto 0.200000 0.214286 0.842759
588 10.000000 rbf ovr 3 auto 0.250000 0.210918 0.802362
589 10.000000 rbf ovr 3 auto 0.300000 0.229814 0.768800
590 10.000000 rbf ovr 4 scale 0.100000 0.962733 0.963953
591 10.000000 rbf ovr 4 scale 0.150000 0.966942 0.966439
592 10.000000 rbf ovr 4 scale 0.200000 0.962733 0.967682
593 10.000000 rbf ovr 4 scale 0.250000 0.952854 0.967060
594 10.000000 rbf ovr 4 scale 0.300000 0.956522 0.964574
595 10.000000 rbf ovr 4 auto 0.100000 0.242236 0.924177
596 10.000000 rbf ovr 4 auto 0.150000 0.252066 0.887508
597 10.000000 rbf ovr 4 auto 0.200000 0.214286 0.842759
598 10.000000 rbf ovr 4 auto 0.250000 0.210918 0.802362
599 10.000000 rbf ovr 4 auto 0.300000 0.229814 0.768800
600 10.000000 poly ovo 2 scale 0.100000 0.968944 0.965817
601 10.000000 poly ovo 2 scale 0.150000 0.971074 0.964574
602 10.000000 poly ovo 2 scale 0.200000 0.962733 0.966439
603 10.000000 poly ovo 2 scale 0.250000 0.955335 0.965196
604 10.000000 poly ovo 2 scale 0.300000 0.962733 0.968925
605 10.000000 poly ovo 2 auto 0.100000 0.975155 0.997514
606 10.000000 poly ovo 2 auto 0.150000 0.975207 0.996271
607 10.000000 poly ovo 2 auto 0.200000 0.968944 0.993785
608 10.000000 poly ovo 2 auto 0.250000 0.960298 0.990056
609 10.000000 poly ovo 2 auto 0.300000 0.960663 0.988191
610 10.000000 poly ovo 3 scale 0.100000 0.968944 0.971411
611 10.000000 poly ovo 3 scale 0.150000 0.971074 0.970168
612 10.000000 poly ovo 3 scale 0.200000 0.962733 0.968925
613 10.000000 poly ovo 3 scale 0.250000 0.955335 0.969546
614 10.000000 poly ovo 3 scale 0.300000 0.962733 0.969546
615 10.000000 poly ovo 3 auto 0.100000 0.968944 0.996892
616 10.000000 poly ovo 3 auto 0.150000 0.975207 0.996271
617 10.000000 poly ovo 3 auto 0.200000 0.962733 0.992542
618 10.000000 poly ovo 3 auto 0.250000 0.957816 0.989434
619 10.000000 poly ovo 3 auto 0.300000 0.960663 0.988191
620 10.000000 poly ovo 4 scale 0.100000 0.962733 0.974518
621 10.000000 poly ovo 4 scale 0.150000 0.966942 0.971411
622 10.000000 poly ovo 4 scale 0.200000 0.965839 0.972654
623 10.000000 poly ovo 4 scale 0.250000 0.957816 0.970789
624 10.000000 poly ovo 4 scale 0.300000 0.962733 0.970168
625 10.000000 poly ovo 4 auto 0.100000 0.968944 0.996892
626 10.000000 poly ovo 4 auto 0.150000 0.971074 0.995649
627 10.000000 poly ovo 4 auto 0.200000 0.962733 0.992542
628 10.000000 poly ovo 4 auto 0.250000 0.960298 0.990056
629 10.000000 poly ovo 4 auto 0.300000 0.958592 0.987570
630 10.000000 poly ovr 2 scale 0.100000 0.968944 0.965817
631 10.000000 poly ovr 2 scale 0.150000 0.971074 0.964574
632 10.000000 poly ovr 2 scale 0.200000 0.962733 0.966439
633 10.000000 poly ovr 2 scale 0.250000 0.955335 0.965196
634 10.000000 poly ovr 2 scale 0.300000 0.962733 0.968925
635 10.000000 poly ovr 2 auto 0.100000 0.975155 0.997514
636 10.000000 poly ovr 2 auto 0.150000 0.975207 0.996271
637 10.000000 poly ovr 2 auto 0.200000 0.968944 0.993785
638 10.000000 poly ovr 2 auto 0.250000 0.960298 0.990056
639 10.000000 poly ovr 2 auto 0.300000 0.960663 0.988191
640 10.000000 poly ovr 3 scale 0.100000 0.968944 0.971411
641 10.000000 poly ovr 3 scale 0.150000 0.971074 0.970168
642 10.000000 poly ovr 3 scale 0.200000 0.962733 0.968925
643 10.000000 poly ovr 3 scale 0.250000 0.955335 0.969546
644 10.000000 poly ovr 3 scale 0.300000 0.962733 0.969546
645 10.000000 poly ovr 3 auto 0.100000 0.968944 0.996892
646 10.000000 poly ovr 3 auto 0.150000 0.975207 0.996271
647 10.000000 poly ovr 3 auto 0.200000 0.962733 0.992542
648 10.000000 poly ovr 3 auto 0.250000 0.957816 0.989434
649 10.000000 poly ovr 3 auto 0.300000 0.960663 0.988191
650 10.000000 poly ovr 4 scale 0.100000 0.962733 0.974518
651 10.000000 poly ovr 4 scale 0.150000 0.966942 0.971411
652 10.000000 poly ovr 4 scale 0.200000 0.965839 0.972654
653 10.000000 poly ovr 4 scale 0.250000 0.957816 0.970789
654 10.000000 poly ovr 4 scale 0.300000 0.962733 0.970168
655 10.000000 poly ovr 4 auto 0.100000 0.968944 0.996892
656 10.000000 poly ovr 4 auto 0.150000 0.971074 0.995649
657 10.000000 poly ovr 4 auto 0.200000 0.962733 0.992542
658 10.000000 poly ovr 4 auto 0.250000 0.960298 0.990056
659 10.000000 poly ovr 4 auto 0.300000 0.958592 0.987570
660 10.000000 sigmoid ovo 2 scale 0.100000 0.186335 0.174021
661 10.000000 sigmoid ovo 2 scale 0.150000 0.181818 0.178372
662 10.000000 sigmoid ovo 2 scale 0.200000 0.180124 0.185208
663 10.000000 sigmoid ovo 2 scale 0.250000 0.183623 0.188316
664 10.000000 sigmoid ovo 2 scale 0.300000 0.184265 0.185830
665 10.000000 sigmoid ovo 2 auto 0.100000 0.242236 0.253574
666 10.000000 sigmoid ovo 2 auto 0.150000 0.252066 0.253574
667 10.000000 sigmoid ovo 2 auto 0.200000 0.214286 0.249223
668 10.000000 sigmoid ovo 2 auto 0.250000 0.210918 0.249223
669 10.000000 sigmoid ovo 2 auto 0.300000 0.229814 0.249223
670 10.000000 sigmoid ovo 3 scale 0.100000 0.186335 0.174021
671 10.000000 sigmoid ovo 3 scale 0.150000 0.181818 0.178372
672 10.000000 sigmoid ovo 3 scale 0.200000 0.180124 0.185208
673 10.000000 sigmoid ovo 3 scale 0.250000 0.183623 0.188316
674 10.000000 sigmoid ovo 3 scale 0.300000 0.184265 0.185830
675 10.000000 sigmoid ovo 3 auto 0.100000 0.242236 0.253574
676 10.000000 sigmoid ovo 3 auto 0.150000 0.252066 0.253574
677 10.000000 sigmoid ovo 3 auto 0.200000 0.214286 0.249223
678 10.000000 sigmoid ovo 3 auto 0.250000 0.210918 0.249223
679 10.000000 sigmoid ovo 3 auto 0.300000 0.229814 0.249223
680 10.000000 sigmoid ovo 4 scale 0.100000 0.186335 0.174021
681 10.000000 sigmoid ovo 4 scale 0.150000 0.181818 0.178372
682 10.000000 sigmoid ovo 4 scale 0.200000 0.180124 0.185208
683 10.000000 sigmoid ovo 4 scale 0.250000 0.183623 0.188316
684 10.000000 sigmoid ovo 4 scale 0.300000 0.184265 0.185830
685 10.000000 sigmoid ovo 4 auto 0.100000 0.242236 0.253574
686 10.000000 sigmoid ovo 4 auto 0.150000 0.252066 0.253574
687 10.000000 sigmoid ovo 4 auto 0.200000 0.214286 0.249223
688 10.000000 sigmoid ovo 4 auto 0.250000 0.210918 0.249223
689 10.000000 sigmoid ovo 4 auto 0.300000 0.229814 0.249223
690 10.000000 sigmoid ovr 2 scale 0.100000 0.186335 0.174021
691 10.000000 sigmoid ovr 2 scale 0.150000 0.181818 0.178372
692 10.000000 sigmoid ovr 2 scale 0.200000 0.180124 0.185208
693 10.000000 sigmoid ovr 2 scale 0.250000 0.183623 0.188316
694 10.000000 sigmoid ovr 2 scale 0.300000 0.184265 0.185830
695 10.000000 sigmoid ovr 2 auto 0.100000 0.242236 0.253574
696 10.000000 sigmoid ovr 2 auto 0.150000 0.252066 0.253574
697 10.000000 sigmoid ovr 2 auto 0.200000 0.214286 0.249223
698 10.000000 sigmoid ovr 2 auto 0.250000 0.210918 0.249223
699 10.000000 sigmoid ovr 2 auto 0.300000 0.229814 0.249223
700 10.000000 sigmoid ovr 3 scale 0.100000 0.186335 0.174021
701 10.000000 sigmoid ovr 3 scale 0.150000 0.181818 0.178372
702 10.000000 sigmoid ovr 3 scale 0.200000 0.180124 0.185208
703 10.000000 sigmoid ovr 3 scale 0.250000 0.183623 0.188316
704 10.000000 sigmoid ovr 3 scale 0.300000 0.184265 0.185830
705 10.000000 sigmoid ovr 3 auto 0.100000 0.242236 0.253574
706 10.000000 sigmoid ovr 3 auto 0.150000 0.252066 0.253574
707 10.000000 sigmoid ovr 3 auto 0.200000 0.214286 0.249223
708 10.000000 sigmoid ovr 3 auto 0.250000 0.210918 0.249223
709 10.000000 sigmoid ovr 3 auto 0.300000 0.229814 0.249223
710 10.000000 sigmoid ovr 4 scale 0.100000 0.186335 0.174021
711 10.000000 sigmoid ovr 4 scale 0.150000 0.181818 0.178372
712 10.000000 sigmoid ovr 4 scale 0.200000 0.180124 0.185208
713 10.000000 sigmoid ovr 4 scale 0.250000 0.183623 0.188316
714 10.000000 sigmoid ovr 4 scale 0.300000 0.184265 0.185830
715 10.000000 sigmoid ovr 4 auto 0.100000 0.242236 0.253574
716 10.000000 sigmoid ovr 4 auto 0.150000 0.252066 0.253574
717 10.000000 sigmoid ovr 4 auto 0.200000 0.214286 0.249223
718 10.000000 sigmoid ovr 4 auto 0.250000 0.210918 0.249223
719 10.000000 sigmoid ovr 4 auto 0.300000 0.229814 0.249223
Row=1, C=0.1, kernel=linear, decision_function_shape=ovo, degree=2, gamma=scale, Test_size=0.15, acc=0.983471, score=0.988813
In [54]:
X_train, X_test, y_train, y_test = train_test_split(Xs, ys, test_size=0.15, random_state=0)
In [55]:
clf_svm = SVC(C=0.1, kernel='linear', decision_function_shape='ovo',degree=2, gamma='scale')
clf_svm.fit(X_train, y_train)
y_pred = clf_svm.predict(X_test)
In [56]:
print('Accuracy = ', metrics.accuracy_score(y_test, y_pred), '\nScore = ', clf_svm.score(Xs, ys))
Accuracy =  0.9834710743801653 
Score =  0.9888129272840274

Best model =>SVM¶

Predict Test data¶

In [57]:
df_test
Out[57]:
id battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi
0 1 1043 1 1.8 1 14 0 5 0.1 193 3 16 226 1412 3476 12 7 2 0 1 0
1 3 1807 1 2.8 0 1 0 27 0.9 186 3 4 1270 1366 2396 17 10 10 0 1 1
2 5 1434 0 1.4 0 11 1 49 0.5 108 6 18 749 810 1773 15 8 7 1 0 1
3 6 1464 1 2.9 1 5 1 50 0.8 198 8 9 569 939 3506 10 7 3 1 1 1
4 7 1718 0 2.4 0 1 0 47 1.0 156 2 3 1283 1374 3873 14 2 10 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
782 994 567 1 2.7 1 14 1 56 0.4 165 8 17 555 1290 336 7 6 7 1 1 1
783 995 936 1 1.4 1 0 0 46 0.8 139 2 0 265 886 684 8 5 12 1 1 1
784 996 1700 1 1.9 0 0 1 54 0.5 170 7 17 644 913 2121 14 8 15 1 1 0
785 999 1533 1 0.5 1 0 0 50 0.4 171 2 12 38 832 2509 15 11 6 0 1 0
786 1000 1270 1 0.5 0 4 1 35 0.1 140 6 19 457 608 2828 9 2 3 1 0 1

787 rows × 21 columns

In [58]:
df_test = df_test.drop('id', axis=1)
In [59]:
predict = clf_svm.predict(df_test)
In [60]:
predict
Out[60]:
array([3, 2, 1, 3, 3, 1, 3, 3, 3, 0, 2, 0, 2, 1, 3, 3, 1, 3, 0, 2, 0, 3,
       0, 2, 0, 3, 0, 0, 1, 3, 1, 1, 1, 2, 0, 0, 1, 3, 1, 1, 0, 0, 3, 1,
       3, 1, 3, 3, 1, 2, 1, 2, 1, 2, 2, 3, 0, 0, 2, 0, 3, 3, 0, 3, 0, 3,
       1, 3, 1, 2, 2, 1, 2, 2, 0, 0, 3, 0, 2, 0, 1, 2, 3, 3, 2, 3, 3, 3,
       2, 3, 0, 0, 3, 2, 1, 2, 0, 2, 3, 2, 2, 1, 1, 3, 1, 1, 1, 1, 2, 3,
       3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 3, 1, 3, 0, 2, 0, 1, 0,
       0, 1, 2, 1, 0, 0, 1, 2, 2, 1, 0, 0, 1, 0, 1, 0, 2, 3, 3, 2, 3, 2,
       3, 2, 1, 1, 0, 1, 2, 0, 2, 3, 0, 2, 0, 3, 2, 3, 0, 1, 0, 3, 0, 0,
       2, 2, 1, 3, 3, 0, 3, 1, 2, 0, 0, 1, 3, 3, 3, 0, 0, 2, 3, 1, 3, 1,
       3, 1, 2, 3, 3, 1, 0, 1, 3, 1, 1, 3, 2, 1, 0, 1, 2, 1, 0, 3, 2, 3,
       3, 2, 3, 3, 2, 1, 1, 0, 2, 0, 0, 3, 0, 3, 0, 1, 2, 0, 2, 3, 1, 2,
       2, 1, 0, 0, 1, 3, 2, 0, 0, 0, 3, 0, 2, 3, 1, 2, 2, 2, 1, 3, 3, 2,
       2, 3, 3, 3, 3, 3, 1, 2, 3, 0, 1, 0, 3, 1, 2, 3, 0, 0, 0, 0, 2, 0,
       2, 2, 2, 2, 0, 0, 0, 3, 0, 3, 2, 2, 1, 2, 3, 1, 1, 2, 0, 1, 0, 3,
       2, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 3, 2, 3, 0, 3, 0, 1, 0, 2, 0, 3,
       2, 3, 3, 1, 3, 1, 3, 3, 2, 0, 1, 2, 1, 0, 0, 1, 2, 1, 0, 3, 2, 0,
       2, 2, 0, 0, 3, 1, 0, 2, 3, 3, 0, 3, 2, 3, 2, 3, 0, 2, 0, 2, 0, 1,
       0, 1, 1, 1, 3, 3, 3, 2, 1, 2, 2, 3, 3, 3, 2, 2, 1, 2, 2, 1, 0, 2,
       2, 0, 0, 0, 3, 1, 0, 2, 2, 2, 0, 3, 2, 2, 1, 0, 3, 0, 1, 3, 1, 1,
       2, 2, 0, 2, 1, 2, 3, 1, 2, 2, 3, 2, 3, 1, 3, 2, 1, 1, 0, 0, 3, 1,
       0, 3, 3, 2, 1, 3, 3, 2, 3, 3, 2, 0, 1, 1, 2, 2, 2, 0, 0, 2, 2, 2,
       2, 1, 3, 3, 0, 1, 3, 2, 1, 1, 0, 0, 2, 1, 0, 1, 2, 0, 2, 2, 1, 0,
       3, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 3, 1, 3, 2, 1, 3, 0, 1, 1, 3, 2,
       2, 0, 3, 0, 2, 0, 0, 1, 1, 2, 1, 1, 3, 2, 1, 3, 0, 2, 2, 3, 3, 0,
       2, 1, 2, 0, 3, 0, 3, 3, 3, 0, 2, 2, 3, 2, 2, 1, 2, 3, 0, 1, 0, 1,
       2, 0, 1, 0, 0, 3, 0, 2, 0, 1, 1, 0, 3, 2, 0, 0, 1, 2, 2, 1, 0, 1,
       2, 0, 1, 1, 0, 0, 3, 0, 3, 1, 3, 0, 1, 0, 2, 1, 0, 1, 1, 3, 2, 0,
       3, 2, 0, 0, 0, 3, 3, 2, 0, 2, 1, 3, 0, 2, 0, 3, 1, 2, 1, 1, 3, 1,
       1, 2, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 2, 3, 0, 1, 1, 0, 1, 0, 2, 0,
       3, 2, 2, 1, 2, 0, 3, 3, 2, 3, 0, 2, 3, 3, 3, 2, 1, 0, 0, 3, 3, 1,
       0, 0, 0, 2, 2, 3, 2, 1, 2, 3, 3, 0, 1, 2, 1, 2, 2, 3, 1, 3, 0, 2,
       3, 2, 1, 1, 1, 3, 0, 2, 2, 3, 2, 2, 3, 2, 0, 1, 2, 1, 2, 2, 2, 1,
       2, 1, 1, 3, 1, 0, 1, 2, 1, 0, 3, 2, 3, 0, 3, 2, 1, 3, 0, 3, 1, 1,
       1, 3, 2, 0, 3, 0, 3, 0, 3, 3, 1, 0, 2, 3, 1, 0, 2, 2, 1, 2, 0, 2,
       2, 0, 2, 2, 3, 0, 2, 1, 1, 2, 2, 3, 3, 0, 2, 1, 2, 3, 1, 1, 3, 0,
       1, 0, 0, 3, 3, 2, 0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2], dtype=int64)
In [61]:
df_test['price_range'] = predict
In [62]:
df_test
Out[62]:
battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen wifi price_range
0 1043 1 1.8 1 14 0 5 0.1 193 3 16 226 1412 3476 12 7 2 0 1 0 3
1 1807 1 2.8 0 1 0 27 0.9 186 3 4 1270 1366 2396 17 10 10 0 1 1 2
2 1434 0 1.4 0 11 1 49 0.5 108 6 18 749 810 1773 15 8 7 1 0 1 1
3 1464 1 2.9 1 5 1 50 0.8 198 8 9 569 939 3506 10 7 3 1 1 1 3
4 1718 0 2.4 0 1 0 47 1.0 156 2 3 1283 1374 3873 14 2 10 0 0 0 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
782 567 1 2.7 1 14 1 56 0.4 165 8 17 555 1290 336 7 6 7 1 1 1 0
783 936 1 1.4 1 0 0 46 0.8 139 2 0 265 886 684 8 5 12 1 1 1 0
784 1700 1 1.9 0 0 1 54 0.5 170 7 17 644 913 2121 14 8 15 1 1 0 2
785 1533 1 0.5 1 0 0 50 0.4 171 2 12 38 832 2509 15 11 6 0 1 0 2
786 1270 1 0.5 0 4 1 35 0.1 140 6 19 457 608 2828 9 2 3 1 0 1 2

787 rows × 21 columns

In [63]:
labels = ['0', '1', '2', '3']
values = df_test['price_range'].value_counts().values

fig = px.pie(values=values,  
             names=labels,
             hole=0.3)

fig.update_layout(
    title_text="Price range of test dataset",
    title_x=0.5, 
    title_yanchor="middle"
)

fig.show()

If you want to run this code again, you will not get my answer. This difference is related to the algorithms used.¶